Journal articles on the topic 'Computer aided diagnosis tools'
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Mun, Seong K., and Dow-Mu Koh. "Special Issue: “Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging”." Diagnostics 12, no. 6 (May 27, 2022): 1331. http://dx.doi.org/10.3390/diagnostics12061331.
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The radiology imaging community has been developing computer-aided diagnosis (CAD) tools since the early 1990s before the imagination of artificial intelligence (AI) fueled many unbound healthcare expectations and other industries [...]
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Ioanovici, Andrei-Constantin, Andrei-Marian Feier, Ioan Țilea, and Daniela Dobru. "Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects." Journal of Interdisciplinary Medicine 2, no. 3 (September 1, 2017): 245–49. http://dx.doi.org/10.1515/jim-2017-0057.
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Abstract Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.
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Molino, F., D. Furia, F. Bar, S. Battista, N. Cappello, and G. Molino. "Computer-Aided Diagnosis in Jaundice: Comparison of Knowledge-based and Probabilistic Approaches." Methods of Information in Medicine 35, no. 01 (January 1996): 41–51. http://dx.doi.org/10.1055/s-0038-1634634.
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AbstractThe study reported in this paper is aimed at evaluating the effectiveness of a knowledge-based expert system (ICTERUS) in diagnosing jaundiced patients, compared with a statistical system based on probabilistic concepts (TRIAL). The performances of both systems have been evaluated using the same set of data in the same number of patients. Both systems are spin-off products of the European project Euricterus, an EC-COMACBME Project designed to document the occurrence and diagnostic value of clinical findings in the clinical presentation of jaundice in Europe, and have been developed as decision-making tools for the identification of the cause of jaundice based only on clinical information and routine investigations. Two groups of jaundiced patients were studied, including 500 (retrospective sample) and 100 (prospective sample) subjects, respectively. All patients were independently submitted to both decision-support tools. The input of both systems was the data set agreed within the Euricterus Project. The performances of both systems were evaluated with respect to the reference diagnoses provided by experts on the basis of the full clinical documentation. Results indicate that both systems are clinically reliable, although the diagnostic prediction provided by the knowledge-based approach is slightly better.
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Bartolini, Ilaria, and Andrea Di Luzio. "CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy." Computers 10, no. 4 (April 13, 2021): 51. http://dx.doi.org/10.3390/computers10040051.
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Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.
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Jiménez-Gaona, Yuliana, María José Rodríguez-Álvarez, and Vasudevan Lakshminarayanan. "Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review." Applied Sciences 10, no. 22 (November 23, 2020): 8298. http://dx.doi.org/10.3390/app10228298.
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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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Lee, Juhun, Robert M. Nishikawa, Ingrid Reiser, and John M. Boone. "Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT." Medical Physics 44, no. 5 (April 13, 2017): 1846–56. http://dx.doi.org/10.1002/mp.12214.
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Ribeiro, Ricardo T., Rui Tato Marinho, and J. Miguel Sanches. "An Ultrasound-Based Computer-Aided Diagnosis Tool for Steatosis Detection." IEEE Journal of Biomedical and Health Informatics 18, no. 4 (July 2014): 1397–403. http://dx.doi.org/10.1109/jbhi.2013.2284785.
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G. P, Vishnu Prasad, Kurapati Vishnu Sai Reddy, A. M. Kiruthik, and Dr J. Arun Nehru. "Prediction of Kidney Stones Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1037–44. http://dx.doi.org/10.22214/ijraset.2022.42416.
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Abstract: Kidney stones are a prevalent disease all over the world, resulting in many of us being rushed to the hospital in excruciating pain. Calculus illness is diagnosed using a variety of imaging modalities. For the interpretation and thorough diagnosis of the photos, specialists are required. Computer-aided diagnosis systems are practical ways that can be utilized as supplemental tools to aid clinicians in their diagnosis. During this project, the deep learning (DL) technique was used to propose an automatic diagnosis of kidney stones using coronal X-ray (CT) pictures, which has made a great advances in the field of AI. Keywords: Kidney stone, medical image, Deep learning, Computed tomography
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Santos, Marcel Koenigkam, José Raniery Ferreira Júnior, Danilo Tadao Wada, Ariane Priscilla Magalhães Tenório, Marcello Henrique Nogueira Barbosa, and Paulo Mazzoncini de Azevedo Marques. "Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine." Radiologia Brasileira 52, no. 6 (December 2019): 387–96. http://dx.doi.org/10.1590/0100-3984.2019.0049.
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Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.
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Owais, Muhammad, Muhammad Arsalan, Tahir Mahmood, Jin Kyu Kang, and Kang Ryoung Park. "Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation." Journal of Medical Internet Research 22, no. 11 (November 26, 2020): e18563. http://dx.doi.org/10.2196/18563.
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Background The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. Objective This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. Methods Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. Results All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. Conclusions This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.
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Huang, Xing, Tsung-Yi Ho, Wenzhong Guo, Bing Li, Krishnendu Chakrabarty, and Ulf Schlichtmann. "Computer-aided Design Techniques for Flow-based Microfluidic Lab-on-a-chip Systems." ACM Computing Surveys 54, no. 5 (June 2021): 1–29. http://dx.doi.org/10.1145/3450504.
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As one of the most promising lab-on-a-chip systems, flow-based microfluidic biochips are being increasingly used for automatically executing various laboratory procedures in biology and biochemistry, such as enzyme-linked immunosorbent assay, point-of-care diagnosis, and so on. As manufacturing technology advances, the characteristic dimensions of biochip systems keep shrinking, and tens of thousands of microvalves can now be integrated into a coin-sized microfluidic platform, making the conventional manual-based chip design no longer applicable. Accordingly, computer-aided design (CAD) of microfluidics has attracted considerable research interest in the EDA community over the past decade. This review article presents recent advances in the design automation of biochips, involving CAD techniques for architectural synthesis, wash optimization, testing, fault diagnosis, and fault-tolerant design. With the help of these CAD tools, chip designers can be released from the burden of complex, large-scale design tasks. Meanwhile, new chip architectures can be explored automatically to open new doors to meet requirements from future large-scale biological experiments and medical diagnosis. We discuss key trends and directions for future research that are related to enable microfluidics to reach its full potential, thus further advancing the development and progression of the microfluidics industry.
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Alexe, Gabriela, James Monaco, Scott Doyle, Ajay Basavanhally, Anupama Reddy, Michael Seiler, Shridar Ganesan, Gyan Bhanot, and Anant Madabhushi. "Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging." Experimental Biology and Medicine 234, no. 8 (August 2009): 860–79. http://dx.doi.org/10.3181/0902-mr-89.
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With the increasing cost effectiveness of whole slide digital scanners, gene expression microarray and SNP technologies, tissue specimens can now be analyzed using sophisticated computer aided image and data analysis techniques for accurate diagnoses and identification of prognostic markers and potential targets for therapeutic intervention. Microarray analysis is routinely able to identify biomarkers correlated with survival and reveal pathways underlying pathogenesis and invasion. In this paper we describe how microarray profiling of tumor samples combined with simple but powerful methods of analysis can identify biologically distinct disease subclasses of breast cancer with distinct molecular signatures, differential recurrence rates and potentially, very different response to therapy. Image analysis methods are also rapidly finding application in the clinic, complementing the pathologist in quantitative, reproducible, detection, staging, and grading of disease. We will describe novel computerized image analysis techniques and machine learning tools for automated cancer detection from digitized histopathology and how they can be employed for disease diagnosis and prognosis for prostate and breast cancer.
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Pavlov, A. E., B. V. Dagbaev, and I. I. Starkova. "COMPREHENSIVE SURVEY OF FREESTYLE WRESTLERS USING COMPUTER-AIDED PULSE DIAGNOSIS SYSTEM (TIBETAN MEDICINE IN SPORTS)." Pedagogical IMAGE 15, no. 1 (2021): 26–37. http://dx.doi.org/10.32343/2409-5052-2021-15-1-26-37.
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Introduction. The study aimed to theoretically substantiate and experimentally test the effectiveness of registering peripheral pulse of freestyle wrestlers, student members of the Russian national team, using the computer-aided pulse diagnosis system. Materials and methods: The paper presents for the first time the results of experimental studies into the heart rate variability with respect to pulse wave during exercise tests using spectral methods, as well as the effect of physical activity on twelve internal organs of an athlete. Research results: The findings indicate the most characteristic deviations in the athlete organism, including a decrease in the function of the digestive organs, and cardiovascular and respiratory systems, due to physical activity. The study provides recommendations to athletes for consultation with specialists. Conclusion: This method can be considered promising for the creation of tools for self-control of the psychophysical state of athletes. Keywords: physical activity, spectral analysis, pulse wave, functional test
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Iakovidis, D. K., T. Goudas, C. Smailis, and I. Maglogiannis. "Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/286856.
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Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.
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Brüllmann, Dan Dominik, Catharina I. D. Weichert, and Monika Daubländer. "Intraoral Cameras as a Computer-Aided Diagnosis Tool for Root Canal Orifices." Journal of Dental Education 75, no. 11 (November 2011): 1452–57. http://dx.doi.org/10.1002/j.0022-0337.2011.75.11.tb05202.x.
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Meira, Marcilio de Oliveira, Anne Magaly de Paula Canuto, Bruno Motta de Carvalho, and Roberto Levi Cavalcanti Jales. "Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT." Research, Society and Development 11, no. 8 (June 29, 2022): e54811831258. http://dx.doi.org/10.33448/rsd-v11i8.31258.
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ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by harmful levels of inattention, disorganization, and/or hyperactivity-impulsivity. In childhood, these symptoms often overlap with those of other disorders, and they tend to persist into adulthood, interfering with relationships and academic and work life. Diagnosis, traditionally made by assessing the patient, i.e., testing and listening to relatives and teachers, has already been aided by neuroimaging. However, the visual analysis of such images to make a psychiatric diagnosis is a complex and sometimes time-consuming task. For this reason, computer-aided diagnostic tools have increasingly evolved that, when combined with machine learning (ML) techniques, can accelerate, facilitate, and maximize the accuracy of diagnoses. Nevertheless, research evaluating ML models for classifying ADHD considering severity using images of the brain SPECT (Single Photon Emission Computed Tomography) is still very sparse. For this reason, this article aims to evaluate the performance of the ML methods: k-NN (k-Nearest Neighbors), Naive Bayes, Decision Tree, MLP (Multilayer Perceptron) and SVM (Support Vector Machine) in the classification of ADHD. The main goal of this analysis is to check whether the subjects have the disorder or not, and to classify the severity of those who have it using SPECT images. A database was created from SPECT images and diagnostic reports. After pre-processing these data, the best hyperparameters for the ML methods were searched, trained/tested and finally statistically compared. The best results were obtained with SVM and k-NN, with 98% accuracy. Although ADHD diagnosis by neuroimaging is not yet a standard clinical procedure, we argue that this study can contribute to ADHD diagnosis research and support methods for the development of CAD (computer-aided diagnosis) systems.
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D’Antoni, Federico, Fabrizio Russo, Luca Ambrosio, Luca Bacco, Luca Vollero, Gianluca Vadalà, Mario Merone, Rocco Papalia, and Vincenzo Denaro. "Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review." International Journal of Environmental Research and Public Health 19, no. 10 (May 14, 2022): 5971. http://dx.doi.org/10.3390/ijerph19105971.
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Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
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Reiter, Alisa Maria Vittoria, Jean Tori Pantel, Magdalena Danyel, Denise Horn, Claus-Eric Ott, and Martin Atta Mensah. "Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study." Journal of Medical Internet Research 26 (March 13, 2024): e42904. http://dx.doi.org/10.2196/42904.
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Background While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism. Objective We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt. Methods Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity. Results DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score’s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores. Conclusions If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.
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Termine, Andrea, Carlo Fabrizio, Carlo Caltagirone, Laura Petrosini, and on behalf of the Frontotemporal Lobar Degeneration Neuroimaging Initiative. "A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks." Life 12, no. 7 (June 23, 2022): 947. http://dx.doi.org/10.3390/life12070947.
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Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators’ attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.
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Noor Najah Ali, Aseel Hameed, Asanka G. Perera, and Ali Al Naji. "Custom YOLO Object Detection Model for COVID-19 Diagnosis." Journal of Techniques 5, no. 3 (September 9, 2023): 92–100. http://dx.doi.org/10.51173/jt.v5i3.1174.
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The emergence and spread of the new coronavirus (COVID-19) poses a new public health threat to the entire world (SARS-CoV-2). This new virus is highly contagious and pathogenetically different from other mainstream respiratory viruses. Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. These tools also help to better understand the course of the disease. In most cases, medical staff and healthcare facilities would be more equipped to promptly diagnose COVID-19 for patients with improved flexibility. To examine the training performance of the contemporary YOLOv4 model, this work presents the development of a computer-assisted automatic detection system that focuses specifically on identifying viral cells in blood samples from patients using electron microscopy images to detect the infected blood cell. The mean average precision of the proposed custom model is 86.5%mAP, making it suitable for the upcoming COVID-19 monitoring systems.
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Owais, Muhammad, Muhammad Arsalan, Tahir Mahmood, Yu Hwan Kim, and Kang Ryoung Park. "Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study." JMIR Medical Informatics 8, no. 12 (December 7, 2020): e21790. http://dx.doi.org/10.2196/21790.
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Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. Objective The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. Methods To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. Results The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. Conclusions This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.
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Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection using CNN LSTM." International Journal of Engineering Research in Electronics and Communication Engineering 9, no. 2 (February 28, 2022): 1–8. http://dx.doi.org/10.36647/ijerece/09.02.a001.
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Liver cancer detection using the computer vision methods and machine learning already received significant attention of researchers for authentic diagnosis and on-time medical attentions. The Computer Aided Diagnosis (CAD) preferred for cancer detection all over the world which is based on image processing service. Earlier CAD tools were designed using conventional machine learning techniue using semi-automatic approach. The modern growth of deep learning for automatic detection and classification leads to significant improvement in accuracy. This paper the automatic CAD framework for liver cancer detection using Convolutional Neural Network (CNN) including Long Short Term Memory (LSTM). The input Computed Tomography (CT) scan images early pre-processed for quality enhancement. After that we applied the lightweight and accuracy field of Interest (ROI) extraction technique using dynamic binary segmentation. From ROI images, we extracted automated CNN-based appearance and hand-craft features. The consolidation of both features formed unique feature set for classification purpose. The LSTM block is then achieve the classification either into normal or diseased CT image. The CNN-LSTM model is designed in this paper to complement the accuracy of liver cancer detection compared to other deep learning solutions. The experimental results of proposed design using CNN-based features and hybrid hand craft features outperformed the recent state-of-art methods.
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Żabińska, Iwona, Artur Kuboszek, Erika Sujova, and Jan Zitnansky. "Ergonomic Diagnosis of a Computer Workstation." Multidisciplinary Aspects of Production Engineering 1, no. 1 (September 1, 2018): 739–44. http://dx.doi.org/10.2478/mape-2018-0093.
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Abstract The article presents the results of research carried out as a part of a project aimed at integrated ergonomic diagnosis of the work environment in terms of improvement of technical and psychosocial conditions. The research carried out so far included small and medium-sized enterprises located in the Śląskie Voivodeship. The tests included blue-collar workers as well as administrative (white-collar) workers. Ergonomic diagnosis was carried out by direct observation of employees at the workplace using tools such as the Ergonomic Control Test CET II and the Dortmund list. This article presents the results of ergonomic analysis at a workstation with a computer.
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Martínez-Murcia, F. J., J. M. Górriz, J. Ramírez, C. G. Puntonet, and D. Salas-González. "Computer Aided Diagnosis tool for Alzheimer’s Disease based on Mann–Whitney–Wilcoxon U-Test." Expert Systems with Applications 39, no. 10 (August 2012): 9676–85. http://dx.doi.org/10.1016/j.eswa.2012.02.153.
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Yang, Huan, and Pengjiang Qian. "GAN-Based Medical Images Synthesis." International Journal of Health Systems and Translational Medicine 1, no. 2 (July 2021): 1–9. http://dx.doi.org/10.4018/ijhstm.2021070101.
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Medical images have always occupied a very important position in modern medical diagnosis. They are standard tools for doctors to carry out clinical diagnosis. However, nowadays, most clinical diagnosis relies on the doctors' professional knowledge and personal experience, which can be easily affected by many factors. In order to reduce the diagnosis errors caused by human subjective differences and improve the accuracy and reliability of the diagnosis results, a practical and reliable method is to use artificial intelligence technology to assist computer-aided diagnosis (CAD). With the help of powerful computer storage capabilities and advanced artificial intelligence algorithms, CAD can make up for the shortcomings of traditional manual diagnosis and realize efficient, intelligent diagnosis. This paper reviews GAN-based medical image synthesis methods, introduces the basic architecture and important improvements of GAN, lists some representative application examples, and finally makes a summary and discussion.
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Tellakula, KK Praneeth, Saravana Kumar R, and Sanjoy Deb. "A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS." Applied Computer Science 17, no. 2 (June 30, 2021): 40–55. http://dx.doi.org/10.35784/acs-2021-12.
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The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals.
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Ziyad, Shabana R., Radha V., and Thavavel Vaiyapuri. "Noise Removal in Lung LDCT Images by Novel Discrete Wavelet-Based Denoising With Adaptive Thresholding Technique." International Journal of E-Health and Medical Communications 12, no. 5 (September 2021): 1–15. http://dx.doi.org/10.4018/ijehmc.20210901.oa1.
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Cancer is presently one of the prominent causes of death in the world. Early cancer detection, which can improve the prognosis and survival of cancer patients, is challenging for radiologists. Low-dose computed tomography, a commonly used imaging test for screening lung cancer, has a risk of exposure of patients to ionizing radiations. Increased radiation exposure can cause lung cancer development. However, reduced radiation dose results in noisy LDCT images. Efficient preprocessing techniques with computer-aided diagnosis tools can remove noise from LDCT images. Such tools can increase the survival of lung cancer patients by an accurate delineation of the lung nodules. This study aims to develop a framework for preprocessing LDCT images. The authors propose a noise removal technique of discrete wavelet transforms with adaptive thresholding by computing the threshold with a genetic algorithm. The performance of the proposed technique is evaluated by comparing with mean, median, and Gaussian noise filters.
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Maiti, Ananjan, Biswajoy Chatterjee, and K. C. Santosh. "Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network." International Journal of Ambient Computing and Intelligence 12, no. 3 (July 2021): 75–97. http://dx.doi.org/10.4018/ijaci.2021070104.
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Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. The experimental images have combined images of naevus (1500), melanoma (1000), and basal cell carcinoma (500). The proposed methods handled issues like class imbalanced with generative adversarial networks (GAN). The recommended color quantization method with synthetic data generation increased the accuracy of the popular machine learning models as it gives an accuracy of 97.08% in random forest. The proposed model preserves a decent accuracy with KNN, adaboost, and gradient boosting.
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Kim, Eun Young, and Myung Jin Chung. "Application of artificial intelligence in chest imaging for COVID-19." Journal of the Korean Medical Association 64, no. 10 (October 10, 2021): 664–70. http://dx.doi.org/10.5124/jkma.2021.64.10.664.
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Background: The coronavirus disease 2019 (COVID-19) pandemic has threatened public health. Medical imaging tools such as chest X-ray and computed tomography (CT) play an essential role in the global fight against COVID-19. Recently emerging artificial intelligence (AI) technologies further strengthen the power of imaging tools and help medical professionals. We reviewed the current progress in the development of AI technologies for the diagnostic imaging of COVID-19.Current Concepts: The rapid development of AI, including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, and drug development. In the era of the COVID-19 pandemic, AI can improve work efficiency through accurate delineation of infections on chest X-ray and CT images, differentiation of COVID-19 from other diseases, and facilitation of subsequent disease quantification. Moreover, computer-aided platforms help radiologists make clinical decisions for disease diagnosis, tracking, and prognosis.Discussion and Conclusion: We reviewed the current progress in AI technology for chest imaging for COVID-19. However, it is necessary to combine clinical experts’ observations, medical image data, and clinical and laboratory findings for reliable and efficient diagnosis and management of COVID-19. Future AI research should focus on multimodality-based models and how to select the best model architecture for COVID-19 diagnosis and management.
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Elzeki, Omar M., Mahmoud Shams, Shahenda Sarhan, Mohamed Abd Elfattah, and Aboul Ella Hassanien. "COVID-19: a new deep learning computer-aided model for classification." PeerJ Computer Science 7 (February 18, 2021): e358. http://dx.doi.org/10.7717/peerj-cs.358.
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Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.
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Maqsood, Sarmad, Robertas Damaševičius, and Rytis Maskeliūnas. "TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages." Applied Sciences 12, no. 7 (March 23, 2022): 3273. http://dx.doi.org/10.3390/app12073273.
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Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.
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Tuncer, Seda Arslan, Ahmet Çınar, and Murat Fırat. "Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images." Traitement du Signal 38, no. 3 (June 30, 2021): 673–79. http://dx.doi.org/10.18280/ts.380314.
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In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In this paper, a hybrid Convolutional Neural Networks-based CAD system, which can classify Diabetic Macular Edema (DME), Drusen Choroidal Neovascularization (CNV), and normal OCT images, is proposed. The proposed system is CNN-SVM (Convolutional Neural Networks – Support Vector Machine) model and doesn’t require any additional extraction of feature or noise filtering on OCT images. A total of 968 OCT images is classified in pre-trained CNN methods with Alexnet, Resnet18 and Googlenet. Accuracy is achieved with highest Googlenet 97.4%. To examine the performance of the proposed CAD system, the CNN-SVM method achieves 98.96% with the highest accuracy hybrid Alexnet-SVM model, which is implemented with Alexnet-SVM, Resnet18-SVM and Googlenet-SVM models.
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Touati, Haifa, Areej Alasiry, Abdulmajid Al-Junaid, Lamia Sellami, Yesmine Ben Hamida, Ahmed Ben Hamida, and Khaireddine Ben Mahfoudh. "Contribution to an Advanced Clinical Aided Tool Dedicated to Explore ASPECTS Score of Ischemic Stroke." Journal of Image and Graphics 12, no. 1 (2024): 40–52. http://dx.doi.org/10.18178/joig.12.1.40-52.
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The Alberta Stroke Program Early CT Score (ASPECTS) is a simple and reliable systematic method used to quantify and explore acute ischemic stroke. It was initially developed to standardize the assessment of the early ischemic changes’ extent within the Middle Cerebral Artery (MCA). The ASPECTS assessment has been increasingly incorporated into treatment decision-making and has been used in several randomized clinical trials for endovascular treatment decision-making. The e-ASPECTS software is a tool for the automated use of ASPECTS. The purpose of this paper is twofold: The first objective is to present an advanced clinical that streamlines the extraction of ASPECTS regions of interest. This tool aids neuro-physicians by automating the segmentation Department process through preprocessing steps involving skull bone stripping, edge detection, and thresholding. The second objective is to propose an automated semi-quantitative method using Non-Contrast Computed Tomography (NCCT), enabling neuro-physicians to accurately diagnose and evaluate acute ischemic stroke. This comprehensive approach improves the exploration, diagnosis, and evaluation of acute ischemic stroke, bolstering clinical decision-making and treatment strategies. Experimental results were promising and depicted an interesting accuracy level ranging from 0.81 (internal capsule) to 0.98 (caudate), with a greater agreement for cortical areas. The proposed automated ASPECTS method presents an independent predictor for clinical practice and ischemic core judgment and treatment selection.
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Sollini, Martina, Margarita Kirienko, Noemi Gozzi, Alessandro Bruno, Chiara Torrisi, Luca Balzarini, Emanuele Voulaz, Marco Alloisio, and Arturo Chiti. "The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the “Real World”?" Cancers 15, no. 2 (January 5, 2023): 357. http://dx.doi.org/10.3390/cancers15020357.
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(1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools’ efficiency; an “intelligent agent” to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnosis (CADx) modules relied on deep learning techniques such as Retina U-Net and the convolutional neural network; (3) Results: Tests were conducted on one publicly available dataset and two local datasets featuring CT scans acquired with different devices to reveal deep learning performances in “real-world” clinical scenarios. The CADe module reached an accuracy rate of 78%, while the CADx’s accuracy, specificity, and sensitivity stand at 80%, 73%, and 85.7%, respectively; (4) Conclusions: Two different deep learning techniques have been adapted for CADe and CADx purposes in both publicly available and private CT scan datasets. Experiments have shown adequate performance in both detection and diagnosis tasks. Nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as CNN and Retina U-Net in real-world clinical scenarios, with CT scans from different devices with different sensors’ fingerprints and spatial resolution. Continuous reassessment of CADe and CADx’s performance is needed during their implementation in clinical practice.
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Kanta Maitra, Indra, and Samir Kumar Bandyopadhyay. "CAD Based Method for Detection of Breast Cancer." Oriental journal of computer science and technology 11, no. 3 (September 10, 2018): 154–68. http://dx.doi.org/10.13005/ojcst11.03.04.
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Breast cancer affecting the women is known to cause high mortality unless detected in right time. Detection requires Mammography followed by biopsy of the tumour or lesions present in the breast tissue. Contemporary Mammographic hardware has incorporated digitization of output imagesfor increasing the scope for implementation of computational methods towards Computer Aided Diagnostics (CAD).CAD systems require Medical Image Processing, a multi-disciplinary science that involves development of computational algorithms on medical images. Histopathological slides are examined for determination of malignancy after biopsy is performed. Digital Images are required to be registered and enhanced prior to application of any deterministic algorithm. This paper provides both effective and efficient improvements over existing algorithms and introduces some innovative ideas based on image segmentation process to develop computer aided diagnosis tools that can help the radiologists in making accurate interpretation of the digital mammograms.
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Marten, K., V. Dicken, C. Kneitz, M. Höhmann, W. Kenn, D. Hahn, and C. Engelke. "Interstitial lung disease associated with collagen vascular disorders: disease quantification using a computer-aided diagnosis tool." European Radiology 19, no. 2 (August 26, 2008): 324–32. http://dx.doi.org/10.1007/s00330-008-1152-1.
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Adarkar, Darshan, Atharva Lokapur, Janhavi Porwal, and Pratik Mali. "Chronic Kidney Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4239–43. http://dx.doi.org/10.22214/ijraset.2023.51239.
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Abstract: Kidney stones play a role in the development of chronic kidney disease. Recurrent kidney stones should be avoided not only because of their immediate clinical manifestations but also because of their long-term predisposition to CKD progression. A lot of people confess to emergency departments with excruciating pain due to kidney stones, which are prevalent ailments around the world. The diagnosis of kidney stone illness involves the use of many imaging modalities. For the entire diagnosis and interpretation of these photos, specialists are required. Systems for computer-aided diagnosis are useful methods that can be utilized as supplemental tools to aid clinicians in their diagnosis. The deep learning (DL) technique, which has lately achieved considerable advancements in the field of artificial intelligence, is offered in this work as a means of automating the detection of kidney stones (containing stones or not) using coronal computed tomography (CT) scans. Different cross-sectional CT images were taken for every individual, resulting in a total of 1453 images. Using CT scans, we have seen that even little kidney stones are accurately detected by our model. This study demonstrates that other difficult urological problems can be addressed using newly popular DL approaches
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Yan Huan, Ch’ng, Mohd Azam Osman, and Jong Hui Ying. "An Innovation-Driven Approach to Specific Language Impairment Diagnosis." Malaysian Journal of Medical Sciences 28, no. 2 (April 21, 2021): 161–70. http://dx.doi.org/10.21315/mjms2021.28.2.15.
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Background: Specific language impairment (SLI) diagnosis is inconvenient due to manual procedures and hardware cost. Computer-aided SLI diagnosis has been proposed to counter these inconveniences. This study focuses on evaluating the feasibility of computer systems used to diagnose SLI. Methods: The accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis accuracy. SLI diagnosis binary classifier was built and tested based on randomised parameters. The obtained results were cross-compared to Singlims_ES.exe for equality. Results: Webgazer.js achieved an average accuracy of 88.755% under global lighting conditions, 61.379% under low lighting conditions and 52.7% under face-focused lighting conditions. The diagnosis task automation script found to execute with actual time delays with a deviation percentage no more than 0.04%, while manually executing time delays based on human time estimation resulted in a deviation percentage of not more than 3.37%. One-tailed test probability value produced by both the newly built classifier and Singlims_ES were observed to be similar up to three decimal places. Conclusion: The results obtained should serve as a foundation for further evaluation of computer tools to help speech language pathologists diagnose SLI.
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Alves, Rui, Marc Piñol, Jordi Vilaplana, Ivan Teixidó, Joaquim Cruz, Jorge Comas, Ester Vilaprinyo, Albert Sorribas, and Francesc Solsona. "Computer-assisted initial diagnosis of rare diseases." PeerJ 4 (July 21, 2016): e2211. http://dx.doi.org/10.7717/peerj.2211.
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Introduction.Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype.Methods.Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis.Results.We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms.Discussion.The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed athttp://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded fromhttps://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.
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D Bonde, Girish, and Dr Manish Jain. "Analysis of MRI Data of Brain for CAD System." International Journal of Engineering & Technology 7, no. 2.17 (April 15, 2018): 63. http://dx.doi.org/10.14419/ijet.v7i2.17.11560.
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Magnetic resonance imaging (MRI) technologies are currently one of the most effective tools in the diagnosis of a wide variety of socially significant pathologies including cancer, arteriosclerosis, episodes. Ischemic and neurodegenerative diseases [1, 2, 3, 4].This paper gives detailed idea of pre-processing, and segmentation(FCM, soft and hard) of MRI brain tumor images. This paper also insights the machine learning(SOM, NN and SVM) approach for automatic classification(PTPSA, fBM) of brain tissues. Different performance evaluation parameter and similarity metrics are discuss to define the efficiency of computer-aided diagnostic (CAD) system.
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Anari, Shokofeh, Nazanin Tataei Sarshar, Negin Mahjoori, Shadi Dorosti, and Amirali Rezaie. "Review of Deep Learning Approaches for Thyroid Cancer Diagnosis." Mathematical Problems in Engineering 2022 (August 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/5052435.
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Thyroid nodule is one of the common life-threatening diseases, and it had an increasing trend over the last years. Ultrasound imaging is a commonly used diagnostic method for detecting and characterizing thyroid nodules. However, assessing the entire slide images is time-consuming and challenging for the experts. For assessing ultrasound images in a meaningful manner, there is a need for automated, trustworthy, and objective approaches. The recent advancements in deep learning have revolutionized many aspects of computer-aided diagnosis (CAD) and image analysis tools that address the problem of diagnosing thyroid nodules. In this study, we explained the objectives of deep learning in thyroid cancer imaging and conducted a literature review on its potential, limits, and current application in this area. We gave an overview of recent progress in thyroid cancer diagnosis using deep learning methods and discussed various challenges and practical problems that might limit the growth of deep learning and its integration into clinical workflow.
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Loh, Hui Wen, Wanrong Hong, Chui Ping Ooi, Subrata Chakraborty, Prabal Datta Barua, Ravinesh C. Deo, Jeffrey Soar, Elizabeth E. Palmer, and U. Rajendra Acharya. "Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)." Sensors 21, no. 21 (October 23, 2021): 7034. http://dx.doi.org/10.3390/s21217034.
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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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Sharma, Vandana, and Divya Midhunchakkaravarthy. "Local post-hoc interpretable machine learning model for prediction of dementia in young adults." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 3 (December 1, 2023): 1569. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1569-1579.
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<p>Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to miti gate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided di agnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare.</p>
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Alharbi, Abir, and F. Tchier. "Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database." Mathematical Biosciences 286 (April 2017): 39–48. http://dx.doi.org/10.1016/j.mbs.2017.02.002.
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Umapathy, Snekhalatha, Sowmiya Vasu, and Nilkantha Gupta. "Computer Aided Diagnosis Based Hand Thermal Image Analysis: A Potential Tool for the Evaluation of Rheumatoid Arthritis." Journal of Medical and Biological Engineering 38, no. 4 (September 30, 2017): 666–77. http://dx.doi.org/10.1007/s40846-017-0338-x.
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Bayat, Nasrin, Diane D. Davey, Melanie Coathup, and Joon-Hyuk Park. "White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization." Big Data and Cognitive Computing 6, no. 4 (October 21, 2022): 122. http://dx.doi.org/10.3390/bdcc6040122.
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Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.
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Hsiao, Chia-Chi, Chen-Hao Peng, Fu-Zong Wu, and Da-Chuan Cheng. "Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT)." Diagnostics 13, no. 24 (December 18, 2023): 3690. http://dx.doi.org/10.3390/diagnostics13243690.
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Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.
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Rojewska, Katarzyna, Stella Maćkowska, Michał Maćkowski, Agnieszka Różańska, Klaudia Barańska, Mariusz Dzieciątko, and Dominik Spinczyk. "Natural Language Processing and Machine Learning Supporting the Work of a Psychologist and Its Evaluation on the Example of Support for Psychological Diagnosis of Anorexia." Applied Sciences 12, no. 9 (May 7, 2022): 4702. http://dx.doi.org/10.3390/app12094702.
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Objective: This study sought to address the use of computer-aided diagnosis and therapy for anorexia nervosa. This paper presents the means by which the use of natural language processing methods can augment the work of psychologists. Method: We evaluated this method based on its efficacy when diagnosing anorexia nervosa. Using natural language processing and machine learning, we developed methods for analyzing five basic emotions, analyzing a patient’s body perception, and detecting six potential areas of difficulties for computer support of psychological diagnosis of anorexia. We surveyed 43 psychologists to obtain feedback on these tools. Results: We evaluated efficacy in terms of patient relationship, substantive aspects of the diagnosis, and diagnostic procedures. In terms of patient relationship, we found a noticeable decrease in the patient’s resistance and better support in verifying the substantive scope of the diagnostic thesis. Discussion: The presented methods can be a supporting tool for monitoring the diagnostic process and increasing the degree of self-diagnosis and self-reflection by the patient. This tool can increase the accuracy of the diagnostic process by reducing patient resistance. This will increase access to the patient’s psychopathology.
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Edith, Hernández-Ovies, and Flores-Preciado Julio César. "Advanced Perspectives in Dentistry: Digital Workflow and 3D Printing." EAS Journal of Dentistry and Oral Medicine 5, no. 06 (December 20, 2023): 198–200. http://dx.doi.org/10.36349/easjdom.2023.v05i06.010.
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The arrival of digital workflow has caused an important evolution in the way dentistry is performed nowadays. Imaging through intraoral scanning systems (IOS) and cone beam computed tomography (CBCT) combined with computer aided design and manufacturing systems (CAD/CAM), make it possible to simplify procedures in all areas of dentistry. Digital techniques are tools that allow more assertive diagnoses, as well as the planning and development of treatments with greater safety, effectiveness and speed. The purpose of this review is to learn about the application of the digital tools that give rise to the development of digital workflow and its general applications in dentistry, in the search for areas of opportunity where this technology can be useful for the development of new techniques that allow that allow for personalized patient-centered care.
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Sandeep, C. S., and A. Sukesh Kumar. "The Different Strategies used for the Early Diagnosis of Alzheimer’s Disease." Asian Journal of Engineering and Applied Technology 8, no. 1 (February 5, 2019): 25–31. http://dx.doi.org/10.51983/ajeat-2019.8.1.1064.
Full textAbstract:
Gerontology or the scientific study of old age deal with the many clinical problems that are common in the elderly population and many of these follow the orthodox pattern of clinical practice. Patients characteristically have poor insight and often attribute their early symptoms of amnesia to normal aging. Alzheimer’s disease (AD) is a common form of senile dementia that makes disabilities in cognitive behavior and performs routine functions. There are several causes for the disease. Although our understanding of the key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by the patient. The aim of this paper is to give an overall idea of the hallmarks, stages of the disease, signs or symptoms and the different methods used for its diagnosis. Any disease-modifying treatments which are developed are most likely to be successful if initiated early in the process, and this requires that we develop reliable, validated and economical ways to diagnose Alzheimer’s−type pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. There are several clinical tests and neuroimaging techniques used in clinical practice for the diagnosis of Alzheimer’s – type pathology. Prominent of them are biomarkers, Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET) and Single−Proton CT Scanning (SPECT). Using the new advanced Biomedical Engineering Technologies to the clinical practices stated above, we can develop a computer-aided tool for the early diagnosis of AD. The different soft computing tools in Biomedical Engineering for developing a computer-aided tool are Neural Networks, Genetic algorithm, Wavelet Networks, Support Vector Machines, and Fuzzy Logic. In this paper, we have focused on the different causes as well as the different strategies used for the early diagnosis of Alzheimer’s disease (AD).