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Hartnick, Maria Diana. "Echocardiography for early detection of heart disease in high risk diabetic patients." Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/1566.
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Masters of Technology: Radiography
in the Faculty of Health and Wellness Sciences
at the Cape Peninsula University of Technology
2015Introduction: Diabetes mellitus is a chronic disease with a significant impact on personal lifestyle and wellbeing. It is associated with a high prevalence of myocardial disease, the early detection of which is important for prevention of disease progression. Although echocardiography is recognised as a leading cardiovascular imaging modality, there has been limited work on its role in the early detection of diabetes-related myocardial dysfunction. The aim of this study was therefore to evaluate the role of echocardiography in the early detection of diabetes-related myocardial disease, in a population with a high prevalence of type 2 diabetes mellitus. Methodology: A single sonographer, blinded to individual biochemical markers conducted detailed echocardiographic examinations on 407 participants from a Cape Town community with a high prevalence of diabetes mellitus. Participants were subsequently stratified by biochemical status, as normoglyceamia or hyperglycaemia. The echocardiographic features of the two groups were compared using the Pearson chi-squared and Mann-Whitney U tests. Findings: Hyperglycaemia was associated with left atrium (LA) enlargement (p ˂ 0.0014), aortic enlargement (p ˂ 0.0067) and inter-ventricular septal (IVS) thickening (p ˂ 0.0001). Conclusion: The findings suggest that echocardiography can be a useful screening tool for myocardial dysfunction in Type 2 diabetes mellitus.
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Kounali, Daphne. "Early growth and coronary heart disease." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436926.
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Springer, David Brian. "Mobile phone-based rheumatic heart disease detection." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:5ec8c818-dafb-4571-8198-97607f8d0451.
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Rheumatic heart disease (RHD), the permanent damage of the heart valves caused by an untreated 'strep throat' infection, is the leading cause of cardiovascular mortality and morbidity in children and young adults worldwide. Simple penicillin treatment after the early diagnosis of RHD can stop recurring bouts of the condition, which lead to the most severe valvulopathy, and ultimately, heart failure. However, RHD is an under-diagnosed condition in the developing world, as such a diagnosis requires, at a minimum, a trained clinician to perform auscultation to detect pathological heart sounds. Trained medical personnel are scarce in the countries where RHD is most prevalent. A low-cost, mobile phone-based automatic diagnostic tool offers a potential solution, allowing a non-medically trained individual to screen for RHD in those countries. An essential feature of such a device is feedback on the signal quality of heart sound recordings. The first major contribution of this thesis is the investigation of features and algorithms for the automatic signal quality assessment of heart sound recordings. These algorithms are able to differentiate between good- and poor-quality recordings in over 80% of cases when using both a low-cost mobile phone-based stethoscope and an electronic stethoscope. Once the quality of recordings is ensured, the positions of the first and second heart sounds need to be located in a process called segmentation. This thesis extends the state-of-the art hidden semi-Markov models by: investigating additional features; extending the Viterbi algorithm; incorporating logistic regression into the model to form a hybrid generative-discriminative model; and investigating a discriminative duration-dependent probabilistic model - a conditional random field. These extensions are found to outperform the state-of-the-art method. Lastly, the period between the first and second heart sounds can be analysed for the presence of a pathological murmur. This thesis presents automated systolic murmur classification algorithms based on wavelet and mel-frequency cepstral coefficient-based features along with denoising via cycle averaging. These algorithms outperform three methods from the literature when detecting valvulopathy, while also outperforming a cardiologist and commercial software when detecting RHD in mobile phone-based heart sound recordings.
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Bull, Adrian Richard. "Early determinants of blood pressure and related disease." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238962.
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Suh, Doug Young. "Knowledge-based boundary detection system : on MRI cardiac image sequences." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13291.
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Pursiainen, V. (Ville). "Autonomic dysfunction in early and advanced Parkinson's disease." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283888.
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Abstract
Parkinson's disease (PD) is known to affect both the extrapyramidal system and the autonomic nervous system even in the early phases of the disease. This study was designed to evaluate cardiovascular autonomic regulation in early PD by measuring heart rate (HR) variability from 24-hour ECG recordings. The dynamics of blood pressure (BP), HR and sweating in patients with and without wearing-off were assessed during clinical observations after a morning dose of levodopa. In patients with wearing-off the tests were repeated after selegiline withdrawal.
The power spectral components of HR variability and the SD1 value of the Poincaré analysis that quantifies the short-term beat-to-beat variability were suppressed at night in the PD patients. During the daytime only the SD1 of the Poincaré was suppressed. The results indicate impairment of parasympathetic cardiovascular regulation in untreated patients with PD. The dysfunction was more pronounced at night and in patients with more severe PD.
The patients with wearing-off had fluctuation of BP during the observation period, BP increasing when the motor performance worsened and vice versa (p < 0.001). The patients without wearing-off did not show fluctuation of BP.
Sweating increased during the observation period, and reached its maximum level at the time of the highest UPDRS motor score phase (off-stage) in patients with wearing-off, but in the patients without wearing-off no changes in sweating were observed. Sweating of the hands was significantly higher in PD patients with motor fluctuations than in those without.
Selegiline withdrawal decreased systolic BP significantly during the on-stage in a supine position as well as during the orthostatic test. The initial drop of BP in the orthostatic test was significantly smaller after selegiline withdrawal. The HR and sweating remained unaffected.
The results show that the autonomic nervous system is affected in the early phases of PD. The dysfunction becomes more pronounced with the disease progression. Wearing-off type motor fluctuations are associated with fluctuation of BP and sweating and these fluctuations may represent autonomic dysfunction caused by PD, the effect of PD medication, or both. Selegiline withdrawal seems to alleviate the orthostatic reaction in patients with advanced PD.
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Shen, Ze-ping. "An application of neural networks for the detection of coronary heart disease." Thesis, Brunel University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385186.
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Heard, Stephanie. "Plant pathogen sensing for early disease control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/plant-pathogen-sensing-for-early-disease-control(48949f80-2596-4ce2-912a-6513e72f6a8d).html.
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Sclerotinia sclerotiorum, a fungal pathogen of over 400 plant species has been estimated to cost UK based farmers approximately £20 million per year during severe outbreak (Oerke and Dehne 2004). S. sclerotiorum disease incidence is difficult to predict as outbreaks are often sporadic. Ascospores released from the fruiting bodies or apothecia can be dispersed for tens of kilometres. This makes disease control problematic and with no S. sclerotiorum resistant varieties available, growers are forced to spray fungicides up to three times per flowering season in anticipation of the arrival of this devastating disease. This thesis reports the development of the first infield S. sclerotiorum biosensor which aims to enable rapid detection of airborne ascospores, promoting a more accurate disease risk assessment and fungicide spraying regime. The sensor is designed to detect the presence of oxalic acid, the main pathogenicity factor secreted during early S. sclerotiorum ascospore germination. Upon electrochemical detection of this analyte in the biosensor, a binary output is relayed to farmer to warm him of a disease risk. This project focused on the development of a nutrient matrix which was designed to be contained within the biosensor. The role of this matrix was to promote the growth of captured airborne S. sclerotiorum ascospores and induce high levels of oxalic acid secretion. The use of the designed biological matrix to promote oxalic acid production was tested during three field trials in S. sclerotiorum artificially inoculated fields. This thesis describes the use of contemporary pathogenomics technologies to further investigate candidate genes involved in pathogenicity alongside the secretion of oxalic acid. A pre-described bioinformatics pipeline was used to predict the S. sclerotiorum secretome to identify potential effector proteins as well as explore proteins which are unique to S. sclerotiorum to be used as other novel targets for detection. GFP tagged constructs were designed to investigate the expression of the putative targets for S. sclerotiorum detection. The transcriptomes of wild type and oxalic acid deficient S. sclerotiorum strains during infection as well as during a saprotrophic stage were investigated. This study provided expression support for not only some of the unannotated genes identified in the putative secretome, but some candidate genes speculated to be involved in infection.
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Mendel, Julian L. "Laurel Wilt Disease: Early Detection through Canine Olfaction and "Omics" Insights into Disease Progression." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3475.
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Laurel wilt disease is a vascular wilt affecting the xylem and water conductivity in trees belonging to the family Lauraceae. The disease was introduced by an invasive species of ambrosia beetle, Xyleborus glabratus. The beetle, together with its newly described fungal symbiont Raffaelea lauricola (pathogenic to host trees), has lead to the devastation and destruction of over 300 million wild redbay trees in southeastern forests. Ambrosia beetles make up a very unique clade of beetle and share a co-evolved obligatory mutualistic relationship with their partner fungi. Rather than consuming host tree material, the beetles excavate galleries or canals within them. These galleries serve two purposes: reproduction and fungal gardening. The beetles house fungal spores within specialized sacs, mycangia, and essentially inoculate host trees with the pathogenic agent. They actively grow and cultivate gardens of the fungus in galleries to serve as their sole food source. Once the fungus reaches the xylem vessels of the host tree, it thrives and leads to the blockage of water flow, both because of fungal accumulation and to the host response of secreting gels, gums and tyloses to occlude vessels in an attempt to quarantine the fungus.
This disease spreads rapidly, and as a result, once symptoms become visible to the naked eye, it is already too late to save the tree, and it has likely already spread to adjacent ones. The present study presents the first documented study involving the early detection of disease from deep within a tree through the use of scent-discriminating canines. In addition, the present study has lead to the development of a novel sample collection device enabling the non-destructive sampling of beetle galleries. Finally, a metabolomics approach revealed key biochemical pathway modifications in the disease state, as well as potential clues to disease development.
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Balderson, Diane E. "Observations on the detection of ventricular late potentials." Thesis, Queen's University Belfast, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238982.
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Sridhara, Bangalore Sitaramiah. "Clinical evaluation of '9'9'mTc tetrofosmin in the detection of ischaemic heart disease." Thesis, University of Surrey, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259561.
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Olsson, Louise. "Early detection of colorectal cancer /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-841-6/.
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Hurrell, Karen Tracy. "Screening for serious disease : modelling the early detection of breast cancer." Thesis, University of Leicester, 1989. http://hdl.handle.net/2381/34546.
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Sriram, Deepa. "Early detection of bowel disease in symptomatic patients attending community pharmacies." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/510.
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With evidence that a substantial number of individuals manage symptoms without seeking medical help, a pharmacy service with symptom management guided by a screening tool should assist in the triage of clients with underlying health conditions. This research describes the successful development, validation and testing of an assessment questionnaire to guide pharmacy staff in identification of pharmacy clients at risk of serious bowel conditions, with referral of clients for further investigation where warranted.
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Cunningham, Adam F. "The detection, epidemiology and immunobiology of Chlamydia pneumoniae." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295711.
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Lopez-Alvarez, Jordi. "Evaluation of early indicators of disease progression in dogs with degenerative mitral valve disease." Thesis, Royal Veterinary College (University of London), 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669194.
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Biba, Matilda. "Colour vision within occupations and in the early detection of retinal disease." Thesis, City University London, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654960.
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Colour is used extensively in the railway industry to enhance conspicuity, code information and group objects of interest together. An acceptable level of colour discrimination is therefore required to enhance visibility even when colour is used redundantly, thus justifying the need to adequately screen all employed personnel. Rail Safety and Standards Board (2002, 2007) state that colour vision must be normal, as assessed by the Ishihara Plates Test or City University test. However, current conventional screening tests are limited in design and have high variability. As a result, a study was commissioned by Transport for London (TfL) to derive empirically, new limits of colour vision loss that could be classed as safe for train operators, within the London Underground (LU) environment. An initial visual task analysis was conducted to indentify the most safety-critical, colour-related task that LU train drivers encountered. This was determined to be the 'within tunnel' signal lights. Unique to this study was the original and faithful laboratory construction and reproduction of the within-tunnel signal light task which made it possible to establish experimentally new colour limits. A total of 100 subjects were recruited for the study, 40 normal trichromats and 60 congenital colour deficient'. They were assessed on conventional screening tests, the Colour Assessment and Diagnosis (CAD) test and compared against the simulated Trains Light (TL) test. Safe colour vision limits were established for two approach distances of 220 m and 110 m. Analysis indicated that for an approach distance of 110 m, deuteranomalous subjects with RG CAD thresholds < 7 and protanomalous subjects with RG CAD thresholds < 10 performed the TL test as well as normal trichromats and were therefore classed as safe to operate a train. An extended analysis of the proposed limits which included previous study data and LU data was conducted. A total of 606 subjects' data was reviewed (normal n = 205, deutan n = 269, protan n = 132) to provide accurate predictive outcomes. Based on the proposed 110 m limits, - 40% of colour deficient applicants would be classed as safe to drive a train. If the approach distance of 220 m were adopted, - 11 % of colour deficient applicants would be classed as safe to conduct a train based on stricter RG CAD thresholds limits of < 2.5 for deuteranomalous subjects and < 9 for protanomalous subjects. TfL have now accepted the CAD limits proposed for an approach distance of 110 m, classifying - 40% of the colour deficient population as safe. The CAD system now replaces the Ishihara test in the LU occupational health centres, and is used during initial and renewal certification of medical fitness assessments. The second study focused on acquired visual function loss associated with ageing and retinal disease. Studies had suggested that visual acuity measurements were not a good predictor of early retinal disease where a significant loss in VA was perceptible only if the majority of the photoreceptors were dysfunctional (Sunness et al., 1997; Gellar et al., 1992). Coupled with the high inter-session measurement variablitiyfor visual acuity (Patel et al., 2008), the aim of the study was to identify the most appropriate and sensitive clinical test capable of detecting early morphological changes associated with early Age-related Macular Degeneration (AMD). A total of 71 subjects were recruited for the study, a normal control group (n = 45) and an active AMD group (n = 26) of varying disease severity. Subjects underwent extensive clinical screening to ensure they adhered to strict,inclusion criteria. Advanced psychophysical assessment of visual function for visual acuity, contrast sensitivity, mesopic and photopic temporal and chromatic sensitivity were conducted. Exclusive to this study was the refinement of a new temporal resolution test. Significant clinical predictors for early-AMD were determined based on bivariate regression analysis and entered stepwise into a multiple linear regression model, controlling for eye and age. Yellow-blue (YB) chromatic discrimination was identified as the best clinical predictor, explaining an additional 9 %-12 % of the variance for early-AMD detection and - 22 %-25 % for AMD disease. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis confirmed YB chromatic sensitivity to be 13.5 % more sensitive and 16.7 % more specific than the standard clinical Log MAR acuity test for detecting early-AMD retinal changes. A review of AMD and diabetic case studies also indicated that accurate Psychophysical methods of assessing chromatic sensitivity enhanced our clinical ability to detect, quantify and monitor changes associated with retinal disease and / or treatment outcomes.
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Howard, Newton. "Approach to study the brain : towards the early detection of neurodegenerative disease." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f81e9d4-ac91-444f-b966-ce1fc665b065.
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Neurodegeneration is a progressive loss of neuron function or structure, including death of neurons, and occurs at many different levels of neuronal circuitry. In this thesis I discuss Parkinson’s Disease (PD), the second most common neurodegenerative disease (NDD). PD is a devastating progressive NDD often with delayed diagnosis due to detection methods that depend on the appearance of visible motor symptoms. By the time cardinal symptoms manifest, 60 to 80 percent or more of the dopamine-producing cells in the substantia nigra are irreversibly lost. Although there is currently no cure, earlier detection would be highly beneficial to manage treatment and track disease progression. However, today’s clinical diagnosis methods are limited to subjective evaluations and observation. Onset, symptoms and progression significantly vary from patient to patient across stages and subtypes that exceed the scope of a standardized diagnosis. The goal of this thesis is to provide the basis of a more general approach to study the brain, investigating early detection method for NDD with focus on PD. It details the preliminary development, testing and validation of tools and methods to objectively quantify and extrapolate motor and non-motor features of PD from behavioral and cognitive output during everyday life. Measures of interest are categorized within three domains: the motor system, cognitive function, and brain activity. This thesis describes the initial development of non-intrusive tools and methods to obtain high-resolution movement and speech data from everyday life and feasibility analysis of facial feature extraction and EEG for future integration. I tested and validated a body sensor system and wavelet analysis to measure complex movements and object interaction in everyday living situations. The sensor system was also tested for differentiating between healthy and impaired movements. Engineering and design criteria of the sensor system were tested for usability during everyday life. Cognitive processing was quantified during everyday living tasks with varying loaded conditions to test methods for measuring cognitive function. Everyday speech was analyzed for motor and non-motor correlations related to the severity of the disease. A neural oscillation detection (NOD) algorithm was tested in pain patients and facial expression was analyzed to measure both motor and non-motor aspects of PD. Results showed that the wearable sensor system can measure complex movements during everyday living tasks and demonstrates sensitivity to detect physiological differences between patients and controls. Preliminary engineering design supports clothing integration and development of a smartphone sensor platform for everyday use. Early results from loaded conditions suggest that attentional processing is most affected by cognitive demands and could be developed as a method to detect cognitive decline. Analysis of speech symptoms demonstrates a need to collect higher resolution spontaneous speech from everyday living to measure speech motor and non-motor speech features such as language content. Facial expression classifiers and the NOD algorithm indicated feasibility for future integration with additional validation in PD patients. Thus this thesis describes the initial development of tools and methods towards a more general approach to detecting PD. Measuring speech and movement during everyday life could provide a link between motor and cognitive domains to characterize the earliest detectable features of PD. The approach represents a departure from the current state of detection methods that use single data entities (e.g.one-off imaging procedures), which cannot be easily integrated with other data streams, are time consuming and economically costly. The long-term vision is to develop a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease.
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Ren, Jinyi [Verfasser], and Michael [Akademischer Betreuer] Ewers. "Automated detection of early-stage Alzheimer’s Disease / Jinyi Ren ; Betreuer: Michael Ewers." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1211957322/34.
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Forsén, Tom. "Early growth and adult disease : programming of coronary heart disease, type 2 diabetes and hypertension by fetal and childhood growth." Helsinki : University of Helsinki, 2000. http://ethesis.helsinki.fi/julkaisut/laa/kansa/vk/forsen/.
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Timmaraju, Venkat Krishna Chaitanya. "Simulating the early detection and intervention of vascular disease in the caerphilly cohort." Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/55711/.
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Introduction: The purpose of the project is to simulate the effect of hypothetical intervention on risk of vascular disease in the Caerphilly cohort. The cohort comprises a total population sample of 2959 men aged 45--59 years at the recruitment who has been followed up for 20 years. During that time there has been particular emphasis on assessing exposure to vascular risk factors and assessing vascular related outcomes. Aim: The aim of the thesis is to estimate the effects at population level of public health interventions to change the levels of modifiable risk factors for the vascular disease. Methods: Various statistical techniques such as logistic, fractional polynomial and Cox's proportional hazards models along with various parametric models were used to analyse the data. New risk prediction models were estimated and compared with the existing models in the literature. Various standard simulation techniques were used to simulate hypothetical data using Caerphilly data parameters. Hypothetical interventions were carried out on these generated samples to assess the public health impact. Results: Multivariate analysis suggested that the combined effect of psychological variables measured in the study were significantly associated with the increased risk of MI. New risk prediction models constructed using the Caerphilly study data showed that they were significantly different from the standard available models from the literature. Simulation results suggested that there could be a reduction MI events by 25--30% and stroke events by 50--55% using plausible intervention scenarios available from the literature review. Conclusion: A hypothetical intervention to modify psychological factors showed a higher reduction in MI events. Therefore, plausible interventions to modify psychological factors should be commissioned along with the standard biological and behavioural interventions.
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Hett, Kilian. "Multi-scale and multimodal imaging biomarkers for the early detection of Alzheimer’s disease." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0011/document.
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La maladie d’Alzheimer est la première cause de démence chez les personnes âgées. Cette maladie est caractérisée par un déclin irréversible des fonctions cognitives. Les patients atteints par la maladie d’Alzheimer ont de sévères pertes de mémoire et ont de grandes difficultés à apprendre de nouvelles informations ce qui pose de gros problèmes dans leur vie quotidienne. À ce jour, cette maladie est diagnostiquée après que d’importantes altérations des structures du cerveaux apparaissent. De plus, aucune thérapie existe permettant de faire reculer ou de stopper la maladie. Le développement de nouvelles méthodes permettant la détection précoce de cette maladie est ainsi nécessaire. En effet, une détection précoce permettrait une meilleure prise en charge des patients atteints de cette maladie ainsi qu’une accélération de la recherche thérapeutique. Nos travaux de recherche portent sur l’utilisation de l’imagerie médicale, avec notamment l’imagerie par résonance magnétique (IRM) qui a démontrée ces dernières années son potentiel pour améliorer la détection et la prédiction de la maladie d’Alzheimer. Afin d’exploiter pleinement ce type d’imagerie, de nombreuses méthodes ont été proposées récemment. Au cours de nos recherches, nous nous sommes intéressés à un type de méthode en particulier qui est basé sur la correspondance de patchs dans de grandes bibliothèques d’images. Nous avons étudié ces méthodes à diverses échelles anatomiques c’est à dire, cerveaux entier, hippocampe, sous-champs de l’hippocampe) avec diverses modalités d’IRM (par exemple, IRM anatomique et imagerie de diffusion). Nous avons amélioré les performances de détection dans les stades les plus précoces avec l’imagerie par diffusion. Nous avons aussi proposé un nouveau schéma de fusion pour combiner IRM anatomique et imagerie de diffusion. De plus, nous avons montré que la correspondance de patchs était améliorée par l’utilisation de filtres dérivatifs. Enfin, nous avons proposé une méthode par graphe permettant de combiner les informations de similarité inter-sujet avec les informations apportées par la variabilité intra-sujet. Les résultats des expériences menées dans cette thèse ont montrées une amélioration des performances de diagnostique et de prognostique de la maladie d’Alzheimer comparé aux méthodes de l’état de l’artAlzheimer’s disease (AD) is the most common dementia leading to a neurodegenerative process and causing mental dysfunctions. According to the world health organization, the number of patients having AD will double in 20 years. Neuroimaging studies performed on AD patients revealed that structural brain alterations are advanced when the diagnosis is established. Indeed, the clinical symptoms of AD are preceded by brain changes. This stresses the need to develop new biomarkers to detect the first stages of the disease. The development of such biomarkers can make easier the design of clinical trials and therefore accelerate the development of new therapies. Over the past decades, the improvement of magnetic resonance imaging (MRI) has led to the development of new imaging biomarkers. Such biomarkers demonstrated their relevance for computer-aided diagnosis but have shown limited performances for AD prognosis. Recently, advanced biomarkers were proposed toimprove computer-aided prognosis. Among them, patch-based grading methods demonstrated competitive results to detect subtle modifications at the earliest stages of AD. Such methods have shown their ability to predict AD several years before the conversion to dementia. For these reasons, we have had a particular interest in patch-based grading methods. First, we studied patch-based grading methods for different anatomical scales (i.e., whole brain, hippocampus, and hippocampal subfields). We adapted patch-based grading method to different MRI modalities (i.e., anatomical MRI and diffusion-weighted MRI) and developed an adaptive fusion scheme. Then, we showed that patch comparisons are improved with the use of multi-directional derivative features. Finally, we proposed a new method based on a graph modeling that enables to combine information from inter-subjects’ similarities and intra-subjects’ variability. The conducted experiments demonstrate that our proposed method enable an improvement of AD detection and prediction
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Valluru, Keerthi Srivastav. "Study of Biomolecular Optical Signatures for Early Disease Detection and Cell Physiology Monitoring." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1213627946.
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Li, Jiangwei. "Applications of single-molecule detection in early disease diagnosis and enzymatic reaction study." [Ames, Iowa : Iowa State University], 2008.
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Telford, Lisa Helen. "Standard echocardiography versus handheld echocardiography for the detection of subclinical rheumatic heart disease: A systematic review." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29528.
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Rheumatic heart disease (RHD) is a permanent heart valve condition resulting from an abnormal immune reaction to group A streptococcal (GAS) infection typically occurring in childhood. If left untreated, disease progression can result in irreversible heart valve damage, cardiac failure, stroke and premature death. Significantly, RHD is a preventable and treatable chronic condition which mostly effects disadvantaged populations across the world. Moreover, the continued persistence of RHD contributes to considerable amounts of preventable morbidity and mortality, predominantly among adolescents and young adults. The accurate detection of subclinical RHD in children and adolescents, however, remains hampered by the cost of diagnostic machinery and scarcity of trained personnel. Alternative RHD screening tests, which are both accurate and affordable, are therefore needed in many endemic areas. Recently, handheld echocardiography has become widely available with a variety of clinical uses. If shown to be sufficiently accurate, use of these handheld devices could potentially expand access to echocardiographic screening in RHD endemic areas. The research undertaken for this MPH dissertation compares the accuracy of handheld echocardiography for the detection of rheumatic heart disease to the reference standard using systematic review methods. The dissertation is structured into three parts.
PART A is a research protocol which describes the background and process of the proposed review. This section details the quantitative methods to be used in the systematic review and meta-analysis of studies which assess the diagnostic accuracy of handheld echocardiography for rheumatic heart disease detection in children and adolescents. The proposed systematic review methods are based on those of the Cochrane Collaboration.
PART B is an extended literature review which expands on some of the topics raised in the background section of the protocol. A more in depth insight into the context surrounding the proposed research is offered and its importance highlighted. By reviewing the current body of evidence, this literature review aimed to both describe and contextualise the global burden of rheumatic heart disease whilst providing a rationale for further research into better screening modalities. Similarly, it also sought to describe the importance of understanding rheumatic heart disease epidemiology so that future research and screening programmes may be targeted accordingly.
PART C is a full systematic review of diagnostic test accuracy studies presented as a journal ‘ready’ manuscript in a format suitable for submission to PLoS ONE. The background to the systematic review is briefly summarised after which the results are then presented and discussed. The main findings, from seven included studies, provide some evidence for the potential of handheld echocardiography to increase access to echocardiographic screening for rheumatic heart disease. Lastly and in conclusion, implications arising from the findings of the review are posited and suggestions for future research offered.
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Sjöberg, Rebecca. "Astrocyte-specific druggable protein as PET-ligand target for early detection of Alzheimer's disease." Thesis, KTH, Skolan för bioteknologi (BIO), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215308.
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Savage, Henry Oluwasefunmi. "Early detection of decompensation of chronic heart failure using a non-contact monitor of nocturnal respiratory patterns." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24577.
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Heart failure affects 1-2% of the adult population in the United Kingdom and accounts for the majority of hospitalisations in patients with cardiovascular disease. The financial implications are enormous as it consumes 1-2% of the national health care budget with 70% of these costs relating to hospitalisation expenses. Prevention of these admissions may be possible by detecting early signs of decompensation in patients with chronic heart failure (CHF) and instituting interventions that may steer the course of disease back to stability without the need for a hospital inpatient stay. Further, Sleep Disordered Breathing (SDB) and in particular Central Sleep Apnoea (CSA) is found in patients with CHF and at any symptomatic stage of the condition. This may be associated with Cheyne-Stokes Respiration (CSR), which has been shown to be an independent predictor of mortality. In the first study of this thesis, I investigated the accuracy of the SleepMinderTM (SM) device; which is a non-contact monitor of nocturnal respiratory patterns; in diagnosing SDB by deriving measures of the Apnoea Hypopnea Index (AHI) and percentage overnight CSR from the SM signals. I found that SM was good in terms of diagnostic accuracy with an area under receiver operator characteristic curve (ROC) of 0.82 (p=0.02) for an AHI threshold >15, but only moderately so for % overnight CSR>0, with an area under ROC curve of 0.72 (p=0.06). In the second study, I examined the changes that occur in SM derived respiratory parameters over a long period of monitoring and found that the AHI, quantity of CSR, Total Sleep Time (TST) and Respiratory Rate (RR) were highly variable with Intra-Class Correlation (ICC) measures of 0.32, 0.39, 0.25, 0.36 respectively over a period of 12 months. Relying on data from a year rather than a single night resulted in misclassification of patients into a different severity group of SDB during 35% of the follow up period and placed patients into a different treatment group during 21% of this period. I also observed that a high proportion (59%) of patients studied had a mean AHI that was consistently above the accepted threshold for treatment (AHI>15). This was consistent even over a shorter follow up period of 2 weeks suggesting that a single night measure of the AHI may not be a sufficient risk assessment of SDB in heart failure patients. In the final study, I have investigated the predictive value of the SleepMinderTM for acute decompensation of heart failure (ADHF) using algorithms derived from its signals. I found that the SM was not accurate for this purpose, performing with a sensitivity and specificity of 0.38 and 0.71, respectively. In summary this study has demonstrated that the SleepMinderTM device provides a novel screening method, which is convenient for the detection of sleep disordered breathing in patients with CHF. It performs with a good diagnostic accuracy and is acceptable to these patients due to its non-contact operation. Algorithms derived from its signals however cannot be used to predict acute decompensation of chronic heart failure. Further, longitudinal analyses of nocturnal respiratory patterns in these patients have demonstrated that the Apnoea Hypopnea Index (AHI) is highly variable over a prolonged period of monitoring and a mean value rather that a single night measurement may be a more appropriate risk assessment tool for SDB. This requires confirmation.
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Palmer, Katie. "Early detection of Alzheimer's disease and dementia in the general population : results from the Kungsholmen project /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7140-055-9/.
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Hamshere, Stephen. "Investigation of the effect of early intracoronary autologous bone marrow cell infusion in the management and treatment of acute myocardial infarction." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30705.
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Cardiovascular disease (CVD) is a complex combination of multiple conditions. The majority of deaths within CVD include heart attacks and strokes caused by atherosclerotic disease. The pathophysiological process for atherosclerotic disease occurs within the endothelial lining of the vessels of the body. This prolonged process occurs when cholesterol deposits form irregularity in luminal flow resulting in decreased blood flow and ischaemia. This unstable cholesterol plaque can rupture resulting in clot formation and artery occlusion. Within this thesis I aim to show background to the relevant pathophysiology of ischaemic heart disease (IHD) with the main emphasis on acute myocardial infarction (AMI), the history of its therapy to current therapy. I will discuss the theorised role of stem cell therapy within animal models and previous clinical trials within regenerative medicine and AMI. I will describe and discuss the method and the results of the REGENERATE-AMI trial (Clintrial.gov: NCT00765453), which will include the safety and efficiency of the therapy, and the possible cytokine mechanism by which this therapy may exert it effect. Additionally I will describe the potential for assessing myocardial oedema using 3-slice T2-STIR short axis stack imaging post AMI compared to the conventional 10-slice T2-STIR technique to assess its feasibility and clinical similarity to assess its use as a tool in translational research.
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Atherton, Daniel Lee. "HYPERSPECTRAL REMOTE SENSING FOR ADVANCED DETECTION OF EARLY BLIGHT (ALTERNARIA SOLANI) DISEASE IN POTATO (SOLANUM TUBEROSUM) PLANTS." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1106.
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Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher’s LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the most mildly diseased plants. Of the analysis methods attempted, cluster analysis and vegetative indices were the most promising. The results show the potential of hyperspectral remote sensing for the detection of early blight in potato plants.
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Brunchault, Valérie. "Identification of biomarkers using-omics approach for the early detection of chronic kidney disease and its complications." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30133/document.
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Diagnostiquer précocement les maladies est un défi à relever pour améliorer la prise en charge des patients concernés et leur offrir une meilleure qualité de vie. Les analyses 'omiques', qui quantifient globalement, simultanément et sans a priori l'abondance de milliers de molécules dans les liquides biologiques, s'avèrent très prometteuses pour l'identification de biomarqueurs précoces des maladies complexes. Dans ce contexte, mon travail de thèse avait pour objectif de développer des outils de diagnostics, à partir d'analyses du peptidome et métabolome urinaire, pour détecter précocement la présence d'une maladie rénale chronique (MRC) et la survenue de ses complications cardiovasculaires. La première étude, insérée dans le projet européen 4C (Cardiovascular Complications in Children with Chronic kidney disease), s'est centrée sur les complications cardiovasculaires associées à la MRC en pédiatrie. Ces complications constituent la principale cause de mortalité des enfants en insuffisance rénale, et leur diagnostic précoce est impossible à ce jour. En analysant par électrophorèse capillaire couplée à la spectrométrie de masse (CE-MS) le peptidome urinaire de 86 enfants souffrant, ou non, de complications cardiovasculaires secondaires à la MRC, nous avons identifié des peptides qui permettent de prédire à l'avance les patients à haut risque cardiovasculaire : 190 peptides étaient associés à l'épaississement de la paroi carotidienne (AUC 0.87, sensibilité 80%, spécificité 100%) et 22 peptides prédisaient l'augmentation de la rigidité artérielle (AUC 0.83, sensibilité 83%, spécificité 70%). Le second projet relevait de la médecine vétérinaire. Dans cette étude menée sur 50 chiens avec et sans MRC, nous avons caractérisé pour la première fois le peptidome urinaire canin via la technologie CE-MS et nous avons découvert 133 peptides urinaires associés à la MRC. Ces derniers ont permis de diagnostiquer la présence d'une MRC dans 80% des chiens. Les métabolites sont mieux corrélés au phénotype que les autres strates moléculaires. Cependant l'apport de la métabolomique en clinique est encore limité, dû au manque de technologies analytiques performantes. Le troisième objectif de ma thèse était donc de mettre au point une procédure de dosage par CE-MS des métabolites urinaires. Grâce à une méthode unique de normalisation interne, basée sur l'utilisation de métabolites endogènes stables, il est maintenant possible d'analyser le contenu en métabolites d'un même échantillon urinaire avec une très haute reproductibilité sur le long terme (4 ans). Comme preuve de concept, nous avons mis en évidence, via cette procédure, la présence d'une combinaison de 32 métabolites dans l'urine qui permet de repérer avec une sensibilité de 76% et une spécificité de 86% les nouveau-nés porteurs d'une malformation rénale obstructive. Enfin, la quatrième problématique s'inscrivait dans une démarche translationnelle. Son but était de développer des aptasenseurs capables de détecter avec de hautes affinités et spécificités les biomarqueurs d'origine omique, pour un diagnostic simple, rapide et à moindre coût. La cible choisie était un fragment urinaire de l'alpha-1-antitrypsine, qui est ~1000 fois plus abondant chez les adultes atteints de MRC que les chez les sains. La sélection de l'aptasenseur s'est faite par le Systematic Evolution of Ligands by EXponential enrichment (SELEX). Nous présentons ici les travaux préliminaires de la mise au point du SELEX sur cette cible. En conclusion, cette thèse démontre le potentiel de l'analyse du contenu urinaire en peptides et métabolites pour le diagnostic précoce des pathologies complexes telles que la MRC et les complications cardiovasculaires associées. De plus l'obtention d'aptasenseurs dirigés contre ces biomarqueurs précoces et utilisables au chevet du patient devrait révolutionner dans le futur les méthodes diagnostiquesEarly diagnosis of diseases is a big challenge to improve patients' health and quality of life. 'Omics' analyses, which allow the global and simultaneous quantification of the relative abundance of thousands of molecules in biological fluids are promising for the identification of early biomarkers of complex diseases. In this context, the objective of my thesis was to develop diagnostic tools, based on urinary peptidome and metabolome analyses, for the early detection of chronic kidney disease (CKD) and associated cardiovascular complications. The first study, as part of the 4C European project (Cardiovascular Complications in Children with Chronic kidney disease), focused on analyzing the cardiovascular complications associated to CKD in children. These complications are the main cause of mortality in children with CKD and their early diagnosis is impossible for now. Analysis of the urinary peptidome of 86 children with or without cardiovascular complications associated to CKD by capillary electrophoresis coupled to mass spectrometry (CE-MS), led to the identification of two sets of peptides for the early prediction of high cardiovascular risk in pediatric patients: 190 peptides were associated to an increase of the carotid intima-media thickness (AUC 0.87, sensitivity 80%, specificity 100%) and 22 peptides were associated to an increase in arterial stiffness (AUC 0.83, sensitivity 83%, specificity 70%). The second study falls in the field of veterinary medicine. In this study, carried out on 50 dogs with or without CKD, we analyzed for the first time the canine urinary peptidome using the CE-MS technology. We identified 133 urinary peptides associated to CKD allowing an accurate diagnosis of CKD in 80% of the dogs. Metabolites correlate best to phenotype compared to other molecular traits. However, the use of metabolomics for identification of clinically relevant biomarkers is very limited due to the lack of high-performance analytical technologies. The third part of my thesis was to develop a procedure for the quantification of urinary metabolites by CE-MS. Using a unique method of internal normalization based on endogenous and stable metabolites, we can now analyze the metabolite content of the same urine sample with a high reproducibility over the long-term (4 years). As a proof-of-concept, we demonstrated that this developed procedure led to the identification of a set of 32 urinary metabolites that allow the early identification of newborns with an obstructive kidney anomaly with a sensitivity of 76% and a specificity of 86%. Finally, the fourth study was dedicated to improving translational research. The aim was to develop aptasensors able to detect 'omics'-identified biomarkers with a high affinity and specificity to obtain a simple, rapid and low-cost diagnostic test. The biomarker chosen as target is a urinary fragment of alpha-1-antitrypsin, which is ~1000 more abundant in adults with CKD compared to healthy subjects. Aptasensors were selected by the Systematic Evolution of Ligands by EXponential enrichment (SELEX). Here we present preliminary work on the development of the SELEX for our target. In conclusion, this thesis shows the strength of the urinary content, in terms of peptides and metabolites, for the early diagnosis of complex pathologies like CKD and the associated cardiovascular complications. Moreover, the selection of aptasensors targeting these early biomarkers and that can be used at bedside, will revolutionize future diagnostic methods
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Gopalappa, Chaitra. "Three Essays on Analytical Models to Improve Early Detection of Cancer." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1647.
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Development of approaches for early detection of cancer requires a comprehensive understanding of the cellular functions that lead to cancer, as well as implementing strategies for population-wide early detection. Cell functions are supported by proteins that are produced by active or expressed genes. Identifying cancer biomarkers, i.e., the genes that are expressed and the corresponding proteins present only in a cancer state of the cell, can lead to its use for early detection of cancer and for developing drugs. There are approximately 30,000 genes in the human genome producing over 500,000 proteins, thereby posing significant analytical challenges in linking specific genes to proteins and subsequently to cancer. Along with developing diagnostic strategies, effective population-wide implementation of these strategies is dependent on the behavior and interaction between entities that comprise the cancer care system, like patients, physicians, and insurance policies. Hence, obtaining effective early cancer detection requires developing models for a systemic study of cancer care.
In this research, we develop models to address some of the analytical challenges in three distinct areas of early cancer detection, namely proteomics, genomics, and disease progression. The specific research topics (and models) are: 1) identification and quantification of proteins for obtaining biomarkers for early cancer detection (mixed integer-nonlinear programming (MINLP) and wavelet-based model), 2) denoising of gene values for use in identification of biomarkers (wavelet-based multiresolution denoising algorithm), and 3) estimation of disease progression time of colorectal cancer for developing early cancer intervention strategies (computational probability model and an agent-based simulation).
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Alhadi, Hafidh A. A. "Role of heart fatty acid binding protein in the early detection of myocardial injury in patients with acute coronary syndromes." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/23007.
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BONZANO, ELISABETTA. "Development of a comprehensive platform for treatment optimization in breast radiotherapy: respiratory gating, heart sparing, early detection of cardiac damage." Doctoral thesis, Università degli studi di Pavia, 2022. http://hdl.handle.net/11571/1463184.
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In the present work, I explored innovative radiotherapy (RT) techniques for treatment optimization in breast cancer patients to spare the heart from radiation exposure and obtain a better and safer treatment strategy.
I have implemented a moderate deep inspiration breast hold (DIBH) radiation technique and prospectively collected data on this treatment to assess the efficacy and advantage of this procedure in left breast cancer treatment.
This study applied a model-based approach to data by calculating the normal tissue complication probability (NTCP) to determine the probability of damage induced on normal tissues for given radiation doses to OAR in terms of cardiac mortality probability.
The second and third parts of the thesis analyzed an alternative heart-sparing technique and a new cardiac damage detection approach. Thus, I described two ongoing trials in which I am our site PI and co-investigator, the first is about cardiac sparing technique for even more selected patients, and the second is about early detection of cardiotoxicity.
In the last part, I introduced a future perspective, with my project-approved proposal on a neoadjuvant RT, a new potential cardiac sparing approach, and an unmet need in our clinical practice.
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Munder, Tonia [Verfasser]. "Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.
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Munder, Tonia Laura [Verfasser]. "Investigation of early histopathological changes in rodent models of Alzheimer's Disease, Parkinson's Disease and CADASIL : brain magnet resonance elastography for early disease detection and staging correlated to histopathology and analysis of neurogenesis and cell survival / Tonia Munder." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1160514887/34.
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Chaung, Monica. "Role of Lung Clearance Index in the Early Detection of Pulmonary Changes in Children with Sickle Cell Disease." Thesis, The University of Arizona, 2018. http://hdl.handle.net/10150/627163.
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A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.Pulmonary complications including acute chest syndrome are leading causes of sickle cell disease related morbidity and mortality. Studies have shown that pulmonary changes can be detected during childhood. Spirometry is the current standard for measuring lung function. Growing evidence suggests that lung clearance index (LCI) is as sensitive as spirometry in identifying pulmonary changes in pediatric patients. Our cross-sectional study compared the sensitivity of LCI to spirometry in the detection of early pulmonary changes in children with sickle cell disease. Our results show that LCI significantly correlates to FEV1% predicted (Spearman’s coefficient -0.44, p = 0.003), FVC % predicted (Spearman’s coefficient -0.44, p = 0.006) and FEF25-75 (Spearman’s coefficient -0.49, p <0.001). Using receiver operating characteristic (ROC) curves, LCI was found to be more sensitive than spirometry, but less specific. The data support LCI’s use as a test to screen for pulmonary changes in children with sickle cell disease. Earlier monitoring of lung function will allow for preventative therapies and delayed progression of pulmonary dysfunction.
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Pan, Tao. "Towards early stage disease detection in microdevices : fabrication and testing of micro total analysis systems for bioanalytical applications / /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1836.pdf.
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Pan, Tao. "Towards Early State Disease Detection in Microdevices: Fabrication and Testing of Micro Total Analysis Systems for Bioanalytical Applications." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1351.
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The past few years have seen a rapid expansion in interest in the characterization of the entire complement of proteins, or proteome. Micro total analysis systems (μTAS) are an emerging promising method, offering rapid, sensitive and low sample consumption separations. I have demonstrated microchip capillary electrophoresis (CE) devices made of CaF2. New methods have been developed for micromachining enclosed capillaries in CaF2. CE analysis of fluorescently labeled amino acids was used to illustrate bioanalytical applications of these microdevices. Initial on-chip infrared spectroscopy results for qualitative analyte identification were achieved in microfluidic CaF2 channels. I have also shown the evaluation of poly(methylmethacrylate) (PMMA) and thermoset polyester (TPE) microchips for use in protein profiling. To improve separation efficiency and reduce protein adsorption, dynamic coating and poly(ethylene glycol) (PEG) grafting using atom transfer radical polymerization (ATRP) have been used in PMMA microdevices. Proteins, peptides and protein digests have been separated electrophoretically in these PMMA microchips. My results demonstrate that PMMA microdevices should be well suited as microfluidic systems for high performance separations of complex biological mixtures. In-channel ATRP has been developed for the surface modification of TPE microdevices. Characterization indicates that PEG-modified microchannels have much lower and more pH-stable electroosmotic flow, more hydrophilic surfaces and reduced nonspecific protein adsorption. CE of amino acid and peptide mixtures in these PEG-modified TPE microchips had good reproducibility. Phosducin-like protein and phosphorylated phosducin-like protein were also separated to measure the phosphorylation efficiency. My results show that PEG-grafted TPE microchips have broad potential application in biomolecular analysis. Cancer marker analysis is important for medical research and applications. I report a method that can covalently attach appropriately oriented antibodies of interest on monolith surfaces. To reduce nonspecific adsorption, protein solutions were used to effectively block the monolith surface. Selective preconcentration and elution of human chorionic gonadotropin have been performed in my affinity columns, demonstrating that this type of system should have promising applications in cancer marker detection.
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Comuni, Federica. "A natural language processing solution to probable Alzheimer’s disease detection in conversation transcripts." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19889.
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This study proposes an accuracy comparison of two of the best performing machine learning algorithms in natural language processing, the Bayesian Network and the Long Short-Term Memory (LSTM) Recurrent Neural Network, in detecting Alzheimer’s disease symptoms in conversation transcripts. Because of the current global rise of life expectancy, the number of seniors affected by Alzheimer’s disease worldwide is increasing each year. Early detection is important to ensure that affected seniors take measures to relieve symptoms when possible or prepare plans before further cognitive decline occurs. Literature shows that natural language processing can be a valid tool for early diagnosis of the disease. This study found that mild dementia and possible Alzheimer’s can be detected in conversation transcripts with promising results, and that the LSTM is particularly accurate in said detection, reaching an accuracy of 86.5% on the chosen dataset. The Bayesian Network classified with an accuracy of 72.1%. The study confirms the effectiveness of a natural language processing approach to detecting Alzheimer’s disease.
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Gyenes, Gábor. "Cardiac side-effects of adjuvant radiotherapy for early breast cancer /." [Budapest] ; Stockholm, 1997. http://diss.kib.ki.se/1997/963-9106-04-6.
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Farajipour, Parisa. "In Vitro Biomarker Detection for Early Diagnosis of Neurodegenerative Diseases via the Ocular Fluid." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1259778648.
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Olsson, Anna, and Denise Nordlöf. "Early screening diagnostic aid for heart disease using data mining : An evaluation using patient data that can be obtained without medical equipment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166593.
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Heart disease is the leading cause of death in the world. Being able to conduct an early screening diagnosis of heart disease at home, could potentially be a tool to reduce the amount of people who lose their lives to the disease in the future. This report aims at investigating if an early screening diagnostic aid using no attributes requiring advanced medical equipment to be measured can be created, that acquires the same level of accuracy as previous data sets and studies. A litera- ture study of medical background, patient data sets and attributes, as well as data mining was conducted. A unique home data set consisting of attributes that can be obtained from home was created and data mining experiments were run in WEKA, using classification algorithms Naive-Bayes and Decision Trees. The results are compared to the Cleveland data set in regards to accuracy. The study shows that the home data set does not deliver the same accuracy level as the Cleveland data set. The idea that similar accuracy can be obtained for the dierent sets has not been disproven and more exhaustive research is encouraged.
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Kodewitz, Andreas. "Methods for large volume image analysis : applied to early detection of Alzheimer's disease by analysis of FDG-PET scans." Thesis, Evry-Val d'Essonne, 2013. http://www.theses.fr/2013EVRY0005/document.
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Dans cette thèse, nous explorons de nouvelles méthodes d’analyse d’images pour la détection précoce des changements métaboliques cérébraux causés par la maladie d’Alzheimer. Nous introduisons deux apports méthodologiques que nous appliquons à un ensemble de données réelles. Le premier est basé sur l’apprentissage automatique afin de créer une carte des informations pertinentes pour la classification d'un ensemble d’images. Pour cela nous échantillonnons des blocs de Voxels selon un algorithme de Monte-Carlo. La mise en œuvre d’une classification basée sur ces patchs 3d a pour conséquence la réduction significative du volume de patchs à traiter et l’extraction de caractéristiques dont l’importance est statistiquement quantifiable. Cette méthode s’applique à différentes caractéristiques et est adaptée à des types d’images variés. La résolution des cartes produites par cette méthode peut être affinée à volonté et leur contenu informatif est cohérent avec des résultats antérieurs obtenus dans la littérature. Le second apport méthodologique porte sur la conception d’un nouvel algorithme de décomposition de tenseur d’ordre important, adapté à notre application. Cet algorithme permet de réduire considérablement la consommation de mémoire et donc en évite la surcharge. Il autorise la décomposition rapide de tenseurs, y compris ceux de dimensions très déséquilibrées. Nous appliquons cet algorithme en tant que méthode d’extraction de caractéristiques dans une situation où le clinicien doit diagnostiquer des stades précoces de la maladie d'Alzheimer en utilisant la TEP-FDG seule. Les taux de classification obtenus sont souvent au-dessus des niveaux de l’état de l’artIn this thesis we want to explore novel image analysis methods for the early detection of metabolic changes in the human brain caused by Alzheimer's disease (AD). We will present two methodological contributions and present their application to a real life data set. We present a machine learning based method to create a map of local distribution of classification relevant information in an image set. The presented method can be applied using different image characteristics which makes it possible to adapt the method to many kinds of images. The maps generated by this method are very localized and fully consistent with prior findings based on Voxel wise statistics. Further we preset an algorithm to draw a sample of patches according to a distribution presented by means of a map. Implementing a patch based classification procedure using the presented algorithm for data reduction we were able to significantly reduce the amount of patches that has to be analyzed in order to obtain good classification results. We present a novel non-negative tensor factorization (NTF) algorithm for the decomposition of large higher order tensors. This algorithm considerably reduces memory consumption and avoids memory overhead. This allows the fast decomposition even of tensors with very unbalanced dimensions. We apply this algorithm as feature extraction method in a computer-aided diagnosis (CAD) scheme, designed to recognize early-stage ad and mild cognitive impairment (MCI) using fluorodeoxyglucose (FDG) positron emission tomography (PET) scans only. We achieve state of the art classification rates
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Ansart, Manon. "Design of data driven decision support systems for the early detection of subjects at risk to develop Alzheimer's disease." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS450.
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Le but de cette thèse est de proposer des méthodes d’apprentissage automatique pour identifier des sujets à risque de développer la maladie d'Alzheimer. L'identification à un stade très précoce de sujets à risque de développer la maladie est une problématique clé, qui permettrait de mieux étudier la maladie, de sélectionner des patients pour des essais cliniques et de leur proposer un suivi adapté. Dans un premier chapitre, nous effectuons une revue des méthodes prédisant le diagnostic futur de sujets atteints de troubles cognitifs légers. Nous effectuons un travail de synthèse, à la fois qualitatif et quantitatif, des méthodes proposées pour effectuer cette prédiction et des problèmes méthodologiques qu'elles comportent. Dans un deuxième chapitre, nous proposons d’effectuer cette prédiction du futur diagnostic avec une approche en deux temps : nous prédisons d'abord l'évolution des caractéristiques des sujets, et utilisons ces résultats pour prédire le diagnostic correspondant à un stade ultérieur. Dans un troisième chapitre, nous proposons une méthode automatique permettant de repérer des sujets à biomarqueurs positifs pour les essais cliniques, de manière à minimiser le coût de recrutement. Dans un dernier chapitre, nous analysons l'évolution des prescriptions de médicaments avant et après le diagnostic grâce à des bases d'historiques médicaux. Nous les utilisons pour prédire si un patient va développer la maladie d'Alzheimer dans les 5 ou 10 années à venir. Nous mettons en avant l’importance de prendre en compte l’adoption des méthodes et leur cadre d’utilisation, notamment à travers la cohorte d’étude, les types de données, et l’interprétabilité de la méthodeThe goal of this thesis is to design data-driven methods to identify subjects at risk to develop Alzheimer's disease. As it is a progressive disease, subtle signs can appear several years before the first clinical symptoms. Identifying subjects who show these signs, and who are likely to develop the disease in the coming years, is a crucial point that could allow researchers to better study the disease mechanism, select patients for clinical trials and tailor patient care. In the first chapter, we conduct a review of methods predicting the future diagnosis of subjects suffering from mild cognitive impairment. We quantitatively and qualitatively study these methods, and take a critical view point by identifying several methodological issues. In the second chapter, we propose our own method to predict the future diagnosis by using a two-step approach: we first predict the future subject characteristics, and then use this result to predict the corresponding diagnosis. In the third chapter, we propose an automatic method to select subjects with a positive biomarker for clinical trials, so as to minimize the recruitment cost. In the last chapter, we analyze prescription patterns before and after diagnosis using a medical record database. We use them to predict if a patient will develop Alzheimer's disease in the next five or ten years. Across these works, we show the importance to take into account the adoption of these methods and the settings in which they can be used, especially regarding the test cohort, the data types and the interpretability of the method
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Milano, Isabel. "The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2192.
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Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models.
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Matthew, Shona. "The development and assessment of cardiac magnetic resonance imaging for the detection of age- and disease-related changes in the human heart." Thesis, University of St Andrews, 2012. http://hdl.handle.net/10023/3558.
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Cardiovascular disease (CVD) is a term used to describe a variety of diseases and events that impact the heart and circulatory system. CVD is the United Kingdom's (UKs) biggest killer, causing more than 50,000 premature deaths each year. Early recognition of the potential for magnetic resonance imaging (MRI) to provide a versatile, non-ionising, non-invasive, technique for the assessment of CVD resulted in the modality becoming an area of intense interest in the research, radiology and cardiology communities. The first half of this thesis reviews some of the key developments in magnetic resonance hardware and software that have led to cardiac magnetic resonance imaging (CMRI) emerging as a reliable and reproducible tool, with a range of applications ideally suited for the evaluation of cardiac morphology, function, viability, valvular disease, perfusion, and congenital cardiomyopathies. In addition to this, the advantages and challenges of imaging at 3.0T in comparison to 1.5T are discussed. The second half of this thesis presents a number of investigations that were specifically designed to explore the capability of CMRI to accurately detect subtle age and disease related changes in the human heart. Our investigations begin with a study at 1.5T that explores the clinical and scientific significance of the less frequently used measure of right ventricular function to test the hypothesis that the inclusion of this data provides a more informative assessment of overall cardiac function. The focus then shifts to imaging at 3.0T and the challenges of optimising cardiac imaging at this field strength are discussed. Normal quantitative parameters of cardiac function are established at this field strength for the left ventricle and the left atrium of local volunteers. These values are used to investigate disease related changes in left ventricle and left atrium of distinct patient cohorts. This work concludes by investigating the impact of gadolinium-based contrast agents on the quantitative parameters of cardiac function.
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Kleiren, Emilie. "Towards an early diagnosis of Alzheimer's disease: development of an ATR-FTIR biosensor for the detection of Abeta toxic conformations." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209415.
Full textAbstract:
As the most prevalent cause of dementia worldwide, Alzheimer’s disease (AD) has become a global issue of public health. By current criteria, diagnosis of this neurodegenerative disorder requires both clinical confirmation of dementia and post-mortem detection of the so-called neurofibrillary tangles and senile plaques in the brain. Yet the main proteinaceous component of these plaques, the amyloid beta peptide (Abeta) is now widely believed to initiate a cascade of events that ultimately leads to Alzheimer’s disease. Besides, extensive evidence supports a pathogenic role of soluble oligomers formed upon Abeta aggregation in the onset of the disease, which, unlike Abeta fibrils, present distinct neurotoxic properties and correlate well with disease progression. Their detrimental effects have been suggested to appear decades before the first signs of cognitive impairment, making them biomarkers of choice in the study of the pathology. Given that present guidelines for AD diagnosis are increasingly considered as ill-defined, reliable and early-stage detection methods taking into account the presence of toxic Abeta species are highly awaited by the medical community. In this regard, this thesis work describes the development of a sensing device aiming at the specific detection of the amyloid beta peptide in solution via recognition by antibodies grafted at the surface of functionalized germanium crystals. This new type of BIA-ATR (Biospecific Interaction Analysis - Attenuated Total Reflection) biosensor resorts on ATR-FTIR (Attenuated Total Reflection - Fourier Transform Infrared) spectroscopy, which is extremely sensitive to the secondary structure of proteins. The ATR mode uses germanium as optical transduction element combined to the evanescent wave principle to allow selective online monitoring of peptide-antibody binding events.
In the first part of this work, evaluation of the photochemistry on germanium optical elements have been the subject of intense research focus. Our investigations led to the elaboration of a quality control of functionalization efficiency based on infrared spectroscopy. We also set up in the lab an original ELISA method for selecting antibodies in terms of their true affinity for the Abeta peptide.
Thereafter binding experiments were carried out on the BIA-ATR sensor using different antibodies and Abeta isoforms, leading to the establishing of a standardized protocol for the detection of molecules of interest. Our results showed that Abeta detected on the biosensor corresponded precisely to antibody-bound peptide, whereas Abeta assemblies, and especially Abeta 1-42 oligomeric conformations, could be discriminated with respect to their spectral signature. This point, which was later confirmed by unsupervised statistical analysis, could be considered as particularly interesting and innovative, since to our knowledge, such conformation-sensitivity has never been observed with existing AD diagnostic methods. Moreover, effective recycling of the functionalized crystals has been demonstrated, which confers thereby a second major advantage to the biosensor.
In parallel to these experiments, a structural characterization study of Abeta species was undertaken in order to generate a database of IR spectra, as reference for future comparative analysis of physiological fluids on the biosensor. ATR-FTIR measurements revealed a strong dependency on the ratio between oligomers and fibrils within a mixture and their relative ratio in antiparallel and parallel beta-sheet content. Interestingly, separation trials of oligomeric entities demonstrated a specific effect of Cu2+ ions on Abeta aggregation. Stabilization of small oligomeric aggregates at equimolar Cu2+:Abeta ratios, which had never been clearly evidenced so far, could help to unravel some aspects of the complex role of copper in AD development.
These investigations illustrate the applicability of the so-called BIA-ATR methodology to online detection of different forms of the Abeta peptide in solution and the potential of this new sensor technology to fulfill current pitfalls in providing a reliable and comprehensive approach of AD diagnosis.
Doctorat en Sciences agronomiques et ingénierie biologique
info:eu-repo/semantics/nonPublished
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Wang, Yan, and 王焱. "Atherosclerotic disease of the carotid, coronary and renal arteries: diagnosis, angioplasty and the effect ofstent surface on early thrombosis and restenosis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B31246060.
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Henriksson, Catrin. "Coronary Heart Disease and Early Decision Making, from Symptoms to Seeking Care : Studies with Focus on Pre-hospital Delay in Acute Myocardial Infarction Patients." Doctoral thesis, Uppsala universitet, Institutionen för medicinska vetenskaper, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-156636.
Full textAbstract:
Despite several investigations and interventions aimed at decreasing the time from symptom onset to medical care seeking in acute myocardial infarction patients, the delay time is still too long for best treatment outcomes. In this thesis, investigations aimed at improving our understanding of the factors influencing delay time are evaluated, as well as attitudes to medical care seeking in patients, relatives and the general public. Additionally, an evaluation was performed to examine whether health-related quality of life had any influence on delay time and re-admissions. Participating patients, relatives and representatives of the general public were generally knowledgeable about acute myocardial infarction (AMI) and its symptomatology. The majority of participants knew about the importance of receiving fast treatment when an AMI occurs. Despite people’s knowledge, several patients and relatives felt uncertain of symptom origin and how to act at symptom onset. Patients commonly consulted an additional person when symptoms did not disappear. However, people appeared to act more appropriately if someone else had chest pain compared to self-experienced symptoms. In patients who had suffered from more than one AMI, poor total health status increased the risk of delaying for more than two hours, but no independent association was found between total health status and re-admissions within the first year post-AMI.