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1
Hassan, Masoud Muhammed, and Sulav Adil Taher. "Analysis and Classification of Autism Data Using Machine Learning Algorithms." Science Journal of University of Zakho 10, no. 4 (November 7, 2022): 206–12. http://dx.doi.org/10.25271/sjuoz.2022.10.4.1036.
Повний текст джерелаАнотація:
Autism is a neurodevelopmental disorder that affects children worldwide between the ages of 2 and 8 years. Children with autism have communication and social difficulties, and the current standardized clinical diagnosis of autism still relies on behaviour-based tests. The rapidly growing number of autistic patients in the Kurdistan Region of Iraq necessitates. However, such data are scarce, making extensive evaluations of autism screening procedures more difficult. For this purpose, the use of machine learning algorithms for this disease to assist health practitioners if formal clinical diagnosis should be pursued was investigated. Data from 515 patients were collected in Dohuk city related to autism screening for young children. Three classification algorithms, namely (DT, KNN, and ANN) were applied to diagnose and predict autism using various rating scales. Before applying the above classifiers, the newly obtained data set was in different ways undergo data reprocessing. Since our data is unbalanced with high dimensionality, we suggest combining SMOTE (Synthetic Minority Hyper sampling Technique) and PCA (Primary Component Analysis) to improve the performance of classification models. Experimental results showed that the combination of PCA and SMOTE methods improved classification performance. Moreover, ANN exceeded the other models in terms of accuracy and F1 score, suggesting that these classification methods could be used to diagnose autism in the future.
2
Blacher, Jan, Katherine Stavropoulos, and Yasamine Bolourian. "Anglo-Latino differences in parental concerns and service inequities for children at risk of autism spectrum disorder." Autism 23, no. 6 (January 7, 2019): 1554–62. http://dx.doi.org/10.1177/1362361318818327.
Повний текст джерелаАнотація:
In an evaluation of Anglo and Latina mothers and their children at risk of autism, this study compared mother-reported child behavioral concerns to staff-observed symptoms of autism. Within Latina mothers, the impact of primary language (English/Spanish), mothers’ education, and child age on ratings of developmental concerns was examined. Participants were 218 mothers (Anglo = 85; Latina = 133) of children referred to a no-cost autism screening clinic. Mothers reported on behavioral concerns, autism symptomology, and services received; children were administered the Autism Diagnostic Observation Schedule by certified staff. Results revealed that Anglo and Latino children did not differ by autism symptoms or classification. However, Anglo mothers reported significantly more concerns than Latina mothers. Within the Latina group, analyses revealed significant interaction effects of language and child age; Spanish-speaking mothers of preschoolers endorsed fewer concerns, while Spanish-speaking mothers of school-aged children endorsed more concerns. Despite these reports, Anglo children with a classification of autism spectrum disorder were receiving significantly more services than Latino children with autism spectrum disorder, suggesting early beginnings of a service divide as well as the need for improved parent education on child development and advocacy for Latino families.
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Kuznetsova, L., M. Brychuk, L. Pogasiy, and K. Zhizhkun. "Features of the influence of playing activities on preschool children with a spectrum of autistic disorders in the process of adaptive physical education." Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports), no. 1(121) (January 29, 2020): 53–59. http://dx.doi.org/10.31392/npu-nc.series15.2019.1(121)20.10.
Повний текст джерелаАнотація:
The article deals with the peculiarities of the mental development of children with autism spectrum disorders, their psychophysical abilities, the formation of cognitive functions, the means of communication, the development of the emotional-volitional sphere, behavior in society. Features of correctional and pedagogical work with autistic children at the present stage are considered. A detailed definition of the definition of "autism", a modern classification of autism, the main features of autistic disorders in all its clinical variants are presented. Statistics on the incidence of autism in the world are provided. The characteristics and peculiarities of psychomotor development in preschool children with autism spectrum disorders and the logic of psychomotor development, the features of psychomotor development, the offered educational and correction tasks are presented. Importance and place of mobile games as the main means of adaptive physical education of preschool children with this nosology have been determined. Mobility games are distributed in the focus on the development of motor skills of preschool children with autism spectrum disorders. A modified classification of mobile games, entertainment, and entertainment that can be used in adaptive physical education and extracurricular forms of preschool-age children with autism spectrum disorders is presented.
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Shihab, Ammar I., Faten A. Dawood, and Ali H. Kashmar. "Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis." Advances in Bioinformatics 2020 (January 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/3407907.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.
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Pradhan, Ashirbad, Victoria Chester, and Karansinh Padhiar. "Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features." Bioengineering 9, no. 10 (October 14, 2022): 552. http://dx.doi.org/10.3390/bioengineering9100552.
Повний текст джерелаАнотація:
Previous research has demonstrated that children with autism walk with atypical ankle kinematics and kinetics. Although these studies have utilized single-segment foot (SSF) data, multisegment foot (MSF) kinematics can provide further information on foot mechanics. Machine learning (ML) tools allow the combination of MSF kinematic features for classifying autism gait patterns. In this study, multiple ML models are investigated, and the most contributing features are determined. This study involved 19 children with autism and 21 age-matched controls performing walking trials. A 34-marker system and a 12-camera motion capture system were used to compute SSF and MSF angles during walking. Features extracted from these foot angles and their combinations were used to develop support vector machine (SVM) models. Additional techniques-S Hapley Additive exPlanations (SHAP) and the Shapley Additive Global importancE (SAGE) are used for local and global importance of the black-box ML models. The results suggest that models based on combinations of MSF kinematic features classify autism patterns with an accuracy of 96.3%, which is higher than using SSF kinematic features (83.8%). The relative angle between the metatarsal and midfoot segments had the highest contribution to the classification of autism gait patterns. The study demonstrated that kinematic features from MSF angles, supported by ML models, can provide an accurate and interpretable classification of autism and control patterns in children.
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Chiappedi, Matteo, Giorgio Rossi, Maura Rossi, Maurizio Bejor, and Umberto Balottin. "Autism and classification systems: a study of 84 children." Italian Journal of Pediatrics 36, no. 1 (2010): 10. http://dx.doi.org/10.1186/1824-7288-36-10.
Повний текст джерела
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Li, Genyuan, Olivia Lee, and Herschel Rabitz. "High efficiency classification of children with autism spectrum disorder." PLOS ONE 13, no. 2 (February 15, 2018): e0192867. http://dx.doi.org/10.1371/journal.pone.0192867.
Повний текст джерела
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Bazyma, Nataliia, Yevheniia Lyndina, Olha Rasskazova, Ganna Kavylina, Olga Litovchenko, and Iryna Hrynyk. "Research of the Problem of Autism and Autistic Disorders: Theoretical Aspect." Revista Romaneasca pentru Educatie Multidimensionala 14, no. 2 (May 9, 2022): 301–17. http://dx.doi.org/10.18662/rrem/14.2/582.
Повний текст джерелаАнотація:
The causes of autism remain insufficiently differentiated. It is unlikely that any single disorder can be considered as the only cause of various symptoms and severity of autistic disorders. Although the specific causes of autism remain unclear, significant progress has been made in understanding the possible mechanisms of the disease. Turning to historical sources, we find that the origin and origin of the term "autism" are associated with forming a system of knowledge on the problem of diagnosis and further therapeutic work with children who need unique approaches to learn and educate. Analysis of the classifications of autism reveals the ambiguity of approaches to them. The first attempts at differentiation in the middle of childhood autism syndrome were clinical classifications based on the syndrome's etiology. They play a significant role in developing adequate approaches to providing medical care to children with autism. Psychological and pedagogical tasks required other approaches to determine, depending on the specific situation, the specialization, strategy, and tactics of correctional work. First of all, there was a search for prognostic signs that would assess the possibilities of mental and social development of children in this category. To this end, some scholars have put forward criteria for assessing speech and intellectual development. The analysis of difficulties of the unanimous possibility of classification on separate indicators of mental development of the child (intelligence, speech, behavior, self-regulation, etc.) can be explained by parallel existence of classifications operating today in world practice.
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Ahmed, Zeyad A. T., Theyazn H. H. Aldhyani, Mukti E. Jadhav, Mohammed Y. Alzahrani, Mohammad Eid Alzahrani, Maha M. Althobaiti, Fawaz Alassery, Ahmed Alshaflut, Nouf Matar Alzahrani, and Ali Mansour Al-madani. "Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models." Computational and Mathematical Methods in Medicine 2022 (April 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/3941049.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
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Mayes, Susan Dickerson, Susan L. Calhoun, Michael J. Murray, Jill D. Morrow, Shiyoko Cothren, Heather Purichia, Kirsten K. L. Yurich, and James N. Bouder. "Use of Gilliam Asperger's Disorder Scale in Differentiating High and Low Functioning Autism and ADHD." Psychological Reports 108, no. 1 (February 2011): 3–13. http://dx.doi.org/10.2466/04.10.15.pr0.108.1.3-13.
Повний текст джерелаАнотація:
Little is known about the validity of Gilliam Asperger's Disorder Scale (GADS), although it is widely used. This study of 199 children with high functioning autism or Asperger's Disorder, 195 with low functioning autism, and 83 with Attention Deficit Hyperactivity Disorder (ADHD) showed high classification accuracy (autism vs ADHD) for clinicians' GADS Quotients (92%), and somewhat lower accuracy (77%) for parents' Quotients. Both children with high and low functioning autism had clinicians' Quotients ( M = 99 and 101, respectively) similar to the Asperger's Disorder mean of 100 for the GADS normative sample. Children with high functioning autism scored significantly higher on the Cognitive Patterns subscale than children with low functioning autism, and the later had higher scores on the remaining subscales: Social Interaction, Restricted Patterns of Behavior, and Pragmatic Skills. Using the clinicians' Quotient and Cognitive Patterns score, 70% of children were correctly identified as having high or low functioning autism or ADHD.
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Safer-Lichtenstein, Jonathan, and Laura Lee McIntyre. "Comparing Autism Symptom Severity Between Children With a Medical Autism Diagnosis and an Autism Special Education Eligibility." Focus on Autism and Other Developmental Disabilities 35, no. 3 (May 25, 2020): 186–92. http://dx.doi.org/10.1177/1088357620922162.
Повний текст джерелаАнотація:
Rates of children identified as having autism spectrum disorder (ASD) continue to increase in both medical and school settings. While procedures for providing a medical diagnosis are relatively consistent throughout the United States, the process for determining special education eligibility under an ASD classification varies by state, with many states adopting looser identification criteria than medical taxonomies. This study included a sample of 73 school-age children with ASD and sought to examine differences in ASD symptom severity, adaptive functioning, and challenging behaviors between those identified in the medical system versus those identified in schools. Results indicate that children identified as having ASD only by their school had less severe clinician-rated ASD symptomatology than children with a medical ASD diagnosis but that caregiver reports of adaptive functioning and challenging behavior did not differ between the two groups. These findings do not appear to have been influenced by demographic factors including caregiver education, household income, or health insurance status. Implications and directions for future research are discussed.
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Ustinova, Nataliya V., and Leyla S. Namazova-Baranova. "Role of Pediatrician in Early Risk Evaluation, Diagnosis and Management of Children with Autism Spectrum Disorders." Current Pediatrics 20, no. 2 (May 18, 2021): 116–21. http://dx.doi.org/10.15690/vsp.v20i2.2255.
Повний текст джерелаАнотація:
The article discusses recent ideas about autism: classification approaches, incidence, etiology and pathogenesis, clinical manifestations and diagnosis, comorbid medical conditions, early detection approaches and medical care for children with autism spectrum disorders. The focus is on the information needed for pediatricians in their practice to provide effective medical care for children with neurodevelopmental disorders.
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Ali, Nur Alisa, Syafeeza A. Radzi, Abd Shukur Jaafar, and Norazlin K. Nor. "ConVnet BiLSTM for ASD Classification on EEG Brain Signal." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 11 (August 31, 2022): 77–94. http://dx.doi.org/10.3991/ijoe.v18i11.30415.
Повний текст джерелаАнотація:
As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a spectrum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results.
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Bölte, Sven, Soheil Mahdi, Petrus J. de Vries, Mats Granlund, John E. Robison, Cory Shulman, Susan Swedo, et al. "The Gestalt of functioning in autism spectrum disorder: Results of the international conference to develop final consensus International Classification of Functioning, Disability and Health core sets." Autism 23, no. 2 (January 29, 2018): 449–67. http://dx.doi.org/10.1177/1362361318755522.
Повний текст джерелаАнотація:
Autism spectrum disorder is associated with diverse social, educational, and occupational challenges. To date, no standardized, internationally accepted tools exist to assess autism spectrum disorder–related functioning. World Health Organization’s International Classification of Functioning, Disability and Health can serve as foundation for developing such tools. This study aimed to identify a comprehensive, a common brief, and three age-appropriate brief autism spectrum disorder Core Sets. Four international preparatory studies yielded in total 164 second-level International Classification of Functioning, Disability and Health candidate categories. Based on this evidence, 20 international autism spectrum disorder experts applied an established iterative decision-making consensus process to select from the candidate categories the most relevant ones to constitute the autism spectrum disorder Core Sets. The consensus process generated 111 second-level International Classification of Functioning, Disability and Health categories in the Comprehensive Core Set for autism spectrum disorder—one body structure, 20 body functions, 59 activities and participation categories, and 31 environmental factors. The Common Brief Core Set comprised 60 categories, while the age-appropriate core sets included 73 categories in the preschool version (0- to 5-year-old children), 81 in the school-age version (6- to 16-year-old children and adolescents), and 79 in the older adolescent and adult version (⩾17-year-old individuals). The autism spectrum disorder Core Sets mark a milestone toward the standardized assessment of autism spectrum disorder–related functioning in educational, administrative, clinical, and research settings.
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Rahman, Md Mokhlesur, Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Shahnorbanun Sahran, Suziyani Mohamed, and Rogayah A. Razak. "A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder." Brain Sciences 10, no. 12 (December 7, 2020): 949. http://dx.doi.org/10.3390/brainsci10120949.
Повний текст джерелаАнотація:
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
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Bogdashina, O. B. "Synaesthesia in Autism." Autism and Developmental Disorders 14, no. 3 (2016): 21–31. http://dx.doi.org/10.17759/autdd.2016140302.
Повний текст джерелаАнотація:
Synaesthesia — a phenomenon of perception, when stimulation of one sensory modality triggers a perception in one or more other sensory modalities. Synaesthesia is not uniform and can manifest itself in different ways. As the sensations and their interpretation vary in different periods of time, it makes it hard to study this phenom¬enon. The article presents the classification of different forms of synaesthesia, including sensory and cognitive; and bimodal and multimodal synaesthesia. Some synaesthetes have several forms and variants of synaesthesia, while others – just one form of it. Although synaesthesia is not specific to autism spectrum disorders, it is quite common among autistic individuals. The article deals with the most common forms of synaesthesia in autism, advantages and problems of synesthetic perception in children with autism spectrum disorders, and provides some advice to parents how to recognise synaesthesia in children with autism.
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Suprajitno, Suprajitno. "EFFECT OF FAMILY EMPOWERMENT IN ENHANCING THE CAPABILITIES OF CHILDREN WITH AUTISM." Belitung Nursing Journal 3, no. 5 (October 30, 2017): 533–40. http://dx.doi.org/10.33546/bnj.113.
Повний текст джерелаАнотація:
Background: Children with autism as individuals have a right to receive developmental needs obtained from parent/caregiver during their stay in the family. The family ability can be improved through empowerment training to provide stimulation for the development of children with autism.Objective: This study aims examine the effect of family empowerment in enhancing the capabilities of children with autism.Methods: The research design used a two-stage quasi-experiment. The first stage was a training for parent/caregiver of children with autism using modules. Training was done three times in the Autism Service Center (PLA) of Blitar City. The second stage was the parent/caregiver provided stimulation to their children at home. There were 33 children selected using total sampling in the PLA of Blitar City on April – August, 2016. Data were analyzed using descriptive statistics and paired t-test.Results: The family ability to stimulate the capability of children with autism in the sense of hearing, vision, motoric, and inviting to play obtained average changes of 61.99%, with average items increased from 18.52 to 30.00. While the increase capabilities of children with autism were categorized into five classification: communication, fulfilling of activity daily living, language-numbers–tactile, psychology, and understanding commands.Conclusion: There was a significant effect of family empowerment in enhancing the capabilities of children with autism. Thus, training to improve the ability of parent/caregiver in caring children with autism needs to be implemented in a planned and gradually manner.
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Maurya, Rachana, and Faziullah Khan. "Cognitive Development in Children with Autism Spectrum Disorder: A Piaget’s Cognitive Developmental Approach." Mind and Society 10, no. 03-04 (April 18, 2021): 117–24. http://dx.doi.org/10.56011/mind-mri-103-420224.
Повний текст джерелаАнотація:
Children with autism spectrum disorder often experience difficulties in cognitive skills related to understating, comprehending, analyzing, synthesizing, evaluating, and differentiating between two objects. The present study objective to investigate the effect of Piaget based cognitive tasks on the cognitive skills of children with autism spectrum disorder (ASD). Eight children with ASD were selected through purposive sampling and assigned for the intervention program. To measure IQ, the Non-Verbal performance test Raven’s colored progressive matrices test was used, and the Indian scale for assessment of autism (ISAA) was used to measure the level of autism spectrum disorder. The IQ was obtained above 80, mild level of ASD, and 6-12 years of the children were placed for this study. The cognitive skills of children were assessed pre- (before) and post- (after intervention). An intervention program based on Piaget’s cognitive tasks was implemented on ASD children for four weeks (six days per week) with 30 minutes per session. The total scores on cognitive skills of ASD children were enhanced in the post-test score. The effects of the Piaget’s cognitive tasks (concrete operational stage: conservation task, classification, and particular reasoning) intervention were most evident in the task performance rating scale on tasks conservation, classification, and particular reasoning. Children with ASD can benefit from the Piaget based cognitive tasks to enhance cognitive skills. The study findings emphasize the effectiveness of the cognitive skills on Piaget based cognitive tasks intervention, which parents may use, psychologists, special educators who work with ASD children.
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Alsaade, Fawaz Waselallah, and Mohammed Saeed Alzahrani. "Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms." Computational Intelligence and Neuroscience 2022 (February 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/8709145.
Повний текст джерелаАнотація:
Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).
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Ward, Samantha L., Karen A. Sullivan, and Linda Gilmore. "Agreement Between a Brief Autism Observational Instrument and Established ASD Measures." Educational and Developmental Psychologist 33, no. 2 (May 20, 2016): 127–38. http://dx.doi.org/10.1017/edp.2016.1.
Повний текст джерелаАнотація:
Objective: Limited time and resources necessitate the availability of accurate, inexpensive and rapid diagnostic aids for Autism Spectrum Disorder (ASD). The Autistic Behavioural Indicators Instrument (ABII) was developed for this purpose, but its psychometric properties have not yet been fully established. Method: The clinician-rated ABII, the Autism Diagnostic Observation Schedule (ADOS), the Childhood Autism Rating Scale – Second Edition, Standard Version (CARS2-ST), and Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnostic criteria were individually administered to children with an independent paediatrician DSM-IV-TR or DSM-5 autism spectrum diagnosis, aged 2-6 years (n = 51, Mchildage = 3.6 years). The agreement between each of the measures on autism diagnostic classification was calculated and compared, and the intercorrelation between the instruments examined. Results: There was significant moderate agreement for the classification of autism between the ABII and the DSM-5, and significant fair agreement between the ABII and ADOS and ABII and CARS2-ST. True positive diagnostic classifications were similar across the ABII (n = 47, 92.2%) and ADOS (n = 45, 88.2%), and significantly higher than the CARS2-ST (n = 30, 58.8%). The ABII total scale score was strongly positively correlated with both the ADOS and CARS2-ST total scores. Conclusion: The ABII's test characteristics were comparable to those of established measures, and the intercorrelations between selected measures support its convergent validity. The ABII could be added to the clinician's toolbox as a screening test.
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Golnik, Allison, Nadia Maccabee-Ryaboy, Peter Scal, Andrew Wey, and Philippe Gaillard. "Shared Decision Making: Improving Care for Children with Autism." Intellectual and Developmental Disabilities 50, no. 4 (August 1, 2012): 322–31. http://dx.doi.org/10.1352/1934-9556-50.4.322.
Повний текст джерелаАнотація:
Abstract We assessed the extent to which parents of children with autism spectrum disorder report that they are engaged in shared decision making. We measured the association between shared decision making and (a) satisfaction with care, (b) perceived guidance regarding controversial issues in autism spectrum disorder, and (c) perceived assistance navigating the multitude of treatment options. Surveys assessing primary medical care and decision-making processes were developed on the basis of the U.S. Department of Health and Human Service's Consumer Assessment of Healthcare Providers and Systems survey. In May 2009, after pilot testing, we sent surveys to 203 parents of children from ages 3 to 18 with International Classification of Diseases–9 and parent-confirmed autism spectrum disorder diagnoses. The response rate was 64%. Controlling for key demographic variables, parents of children with autism spectrum disorder reporting higher levels of shared decision making reported significantly greater satisfaction with the overall quality of their child's health care (p ≤ .0001). Parents reporting higher levels of shared decision making were also significantly more likely to report receiving guidance on the many treatment options (p = .0002) and controversial issues related to autism spectrum disorder (p = .0322). In this study, shared decision making was associated with higher parent satisfaction and improved guidance regarding treatments and controversial issues within primary care for children with autism spectrum disorder.
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BRICOUT, Véronique-Aurélie, Marion PACE, Léa DUMORTIER, Sahal MIGANEH, Yohan MAHISTRE, and Michel GUINOT. "Motor Capacities in Boys with High Functioning Autism: Which Evaluations to Choose?" Journal of Clinical Medicine 8, no. 10 (September 21, 2019): 1521. http://dx.doi.org/10.3390/jcm8101521.
Повний текст джерелаАнотація:
The difficulties with motor skills in children with autism spectrum disorders (ASD) has become a major focus of interest. Our objectives were to provide an overall profile of motor capacities in children with ASD compared to neurotypically developed children through specific tests, and to identify which motor tests best discriminate children with or without ASD. Twenty-two male children with ASD (ASD—10.7 ± 1.3 years) and twenty controls (CONT—10.0 ± 1.6 years) completed an evaluation with 42 motor tests from European Physical Fitness Test Battery (EUROFIT), the Physical and Neurological Exam for Subtle Signs (PANESS) and the Movement Assessment Battery for Children ( M-ABC). However, it was challenging to design a single global classifier to integrate all these features for effective classification due to the issue of small sample size. To this end, we proposed a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from different motor assessments. In the ASD group, flexibility, explosive power and strength scores (p < 0.01) were significantly lower compared to the control group. Our results also showed significant difficulties in children with ASD for dexterity and ball skills (p < 0.001). The principal component analysis and agglomerative hierarchical cluster analysis allowed for the classification of children based on motor tests, correctly distinguishing clusters between children with and without motor impairments.
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Remanlay, Henry. "WORKING WITH AUTISM CHILDREN USING ACUPUNCTURE METHOD." Journal Of Vocational Health Studies 2, no. 2 (January 22, 2019): 91. http://dx.doi.org/10.20473/jvhs.v2.i2.2018.91-94.
Повний текст джерелаАнотація:
Background: Autism is a disorder with symptoms as a failure to develop normal social interaction with other people, impaired of communication and imaginative ability, followed by repetitive and stereotyped movements. Autism is a global issue that may a possible cause of generation lost, and economic burden to a country. Acupuncture as one of TCM (Traditional Chinese Medicine) technique is an option to improve the life quality of children with Autism. Purpose: To determine the syndrome pattern of children with autism and how acupuncture method works for children with autism, from the perspective of TCM. Method: Four examination methods are incorporated into nine ongoing-treatment subjects fit in autism classification from randomized special need cases. Subject characteristics are derived from allo-anamnesis. Needle acupuncture was the method of choice except one subject prefers laser acupuncture. The objective is to eliminate the phlegm, calm the heart fire, and tonify spleen. Result: after 3-5 sessions of treatment, parents reported speech improvement and reduction of compulsive self-stimulation behavior. Four subjects demonstrated speech improvement, 1 subject showed better focus and concentration, 1 subject indicated a reduction of compulsive self-stimulation behavior, 1 subject improved in obedience and improved comprehension was found in 2 subjects. Conclusion: Observation on nine subjects showed in general that they had phlegm harassing the heart and digestion problem due to spleen deficiency. This phlegm disturbed the heart functions, i.e. mental capacity including speech. Results from the treatments showed elimination of phlegm, cooling down the heart fire, and tonification of spleen improved speech, focus, comprehension, obedience, and reduction of compulsive self-stimulation behavior. Further research and study from the Chinese medicine perspective are needed.
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Volkmar, Fred R., Donald J. Cohen, Yoshihiko Hoshino, Richard D. Rende, and Rhea Paul. "Phenomenology and classification of the childhood psychoses." Psychological Medicine 18, no. 1 (February 1988): 191–201. http://dx.doi.org/10.1017/s0033291700002014.
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SynopsisTwo hundred and twenty-eight cases of children with final clinical diagnoses of childhood psychosis were reviewed using a standard coding scheme; cases were grouped in three broad categories on the basis of clinical diagnosis (autistic, atypical and schizophreniform). These three groups differed significantly in many respects, although the ‘atypical’ group more closely resembled the autistic group. While it was possible meaningfully to differentiate diagnostic groups using DSM-III criteria, some cases were difficult to classify. Childhood schizophrenia, as strictly defined, was far less common than childhood autism. The development of diagnostic schemes for those children whose disorders are difficult to classify is an important topic for future research.
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Tkachuk, E. A. "Using the syndrome approach to the diagnosis of autism in children." Meditsinskiy sovet = Medical Council, no. 12 (July 12, 2022): 200–204. http://dx.doi.org/10.21518/2079-701x-2022-16-12-200-204.
Повний текст джерелаАнотація:
Autism spectrum disorders in children is a very urgent problem. Today, there is an increase in the number of children suffering from autism spectrum disorders around the world. The relevance of early diagnosis of autism spectrum disorders for timely treatment and correction is high. However, the diagnostic criteria for ASD require an assessment of certain psychomotor skills in a child, which mature much later than necessary for a timely diagnosis, so the diagnosis is made by 5-7 years. New clinical guidelines suggest that primary screening should be performed by a pediatrician. However, practice shows that in reality this does not happen. The reason for this is not only a contradiction in the diagnostic criteria, but also the current ICD-10 classification, which does not reflect the pathogenetic processes in the child's body. Despite this, new clinical guidelines recognize the role of genetic disorders and epigenetic factors in the development of autism spectrum disorders. In this regard, it is proposed to consider autism spectrum disorders as a syndrome that accompanies various genetic disorders, both chromosomal and monogenic anomalies. This approach enables early diagnosis of autism spectrum disorders, as well as the development of treatment and correction methods based on pathogenetic disorders. Currently, more than 100 genes associated with autism are known. In the above studies, it was noted that the polygenic nature of disorders in autism does not allow focusing on phenotypic features. Probably, this is the reason for the difficulties in diagnosing autism by external signs and the low efficiency of the currently known screening methods for diagnosing autism spectrum disorders. Therefore, autism spectrum disorders must be considered from the point of view of pathogenetic changes in the child's body, usually of a hereditary nature, which will allow us to offer effective methods of diagnosis, treatment and correction.
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Pavithra, D., and A. N. Jayanthi. "An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 3028–37. http://dx.doi.org/10.1166/jmihi.2021.3893.
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Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.
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Ikermane, Mohamed, and Abdelkrim El Mouatasim. "Web-based autism screening using facial images and convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (February 1, 2023): 1140. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp1140-1147.
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Developmental disabilities such as autism spectrum disorder (ASD) affect a person’s ability to interact socially, and communicate effectively and also cause behavioral issues. Children with ASD cannot be cured but they might benefit from early intervention to enhance their cognitive abilities, favorite their growth , and affect their lives and families in a positive way. Multiple standard ASD screening tools are used such as the autism diagnostic observational schedule (ADOS) and the autism diagnostic interview (ADI), which are known to be lengthy and challenging without specialist training to administrate and score. The process of ASD assessment can be time-consuming and costly, and the growing number of autistic cases worldwide indicates an urgent need for a quick, simple, and dependable self-administered autism screening tool that may be used if a child displays some of the common signs of autism, and to ensure whether or not he should seek professional full ASD diagnosis. According to a number of studies, ASD individuals exhibit facial phenotypes that are distinct from those of normally developing children. Furthermore, convolutional neural networks (CNN) have mostly found utility in image classification applications due to their high classification accuracy. Using facial images, a dense convolutional network (Densenet) model, and cloud-based advantages, in this paper we proposed a practical, fast, and easy-to-use ASD online screening approach. Easily available through the internet via the link “https://asd-detector.herokuapp.com/”, our suggested web-based screening instrument may be a practical and trustworthy tool for practitioners in their ASD diagnostic procedures with a 98 percent testing dataset classification accuracy.
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Wang, Haishuai, and Paul Avillach. "Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning." JMIR Medical Informatics 9, no. 4 (April 7, 2021): e24754. http://dx.doi.org/10.2196/24754.
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Background In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. Objective Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. Methods After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. Results The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools. Conclusions Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.
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Muhathir, Muhathir, Rizki Muliono, and Merri Hafni. "Image Classification of Autism Spectrum Disorder Children Using Naïve Bayes Method With Hog Feature Extraction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 5, no. 2 (January 26, 2022): 494–501. http://dx.doi.org/10.31289/jite.v5i2.6365.
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Autism Spectrum Disorder (ASD) is a developmental disorder that affects a person's ability to communicate and interact socially. Every year, the number of people diagnosed with Autism Spectrum Disorder rises, necessitating early detection in order to limit the number of people affected and provide proper treatment. As a result, a system was developed in this study to detect Autism Spectrum Disorder in facial photos utilizing versions of the Nave Bayes approach and HoG feature extraction. HoG feature extraction is a local intensity gradient distribution or edge direction perpendicular to the gradient direction without influencing the geometric and photometric transformations, and Nave Bayes is a method that classifies images based on probability. The experimental results of three types of naive Bayes, Bernoulli naive Bayes is the most reliable than Multinomial naive Bayes and Gaussian Naive Bayes. Accuracy, Precision, Recall, and the highest F1-Score using this method, with each value of 89.72%; 90.54%; 89.72%; and 89.9%. The next best performing Gaussian Naive Bayes, the most laborious results were obtained using Naive Bayes multinomials, which had Accuracy, Precision, Recall, and F1-Score of 65.91% each; 68.09%; 65.91%, and 64.19%.
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Overwater, Iris E., André B. Rietman, Sabine E. Mous, Karen Bindels-de Heus, Dimitris Rizopoulos, Leontine W. ten Hoopen, Thijs van der Vaart, et al. "A randomized controlled trial with everolimus for IQ and autism in tuberous sclerosis complex." Neurology 93, no. 2 (June 19, 2019): e200-e209. http://dx.doi.org/10.1212/wnl.0000000000007749.
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ObjectiveTo investigate whether mammalian target of rapamycin inhibitor everolimus can improve intellectual disability, autism, and other neuropsychological deficits in children with tuberous sclerosis complex (TSC).MethodsIn this 12-month, randomized, double-blind, placebo-controlled trial, we attempted to enroll 60 children with TSC and IQ <80, learning disability, special schooling, or autism, aged 4–17 years, without intractable seizures to be assigned to receive everolimus or placebo. Everolimus was titrated to blood trough levels of 5–10 ng/mL. Primary outcome was full-scale IQ; secondary outcomes included autism, neuropsychological functioning, and behavioral problems.ResultsThirty-two children with TSC were randomized. Intention-to-treat analysis showed no benefit of everolimus on full-scale IQ (treatment effect −5.6 IQ points, 95% confidence interval −12.3 to 1.0). No effect was found on secondary outcomes, including autism and neuropsychological functioning, and questionnaires examining behavioral problems, social functioning, communication skills, executive functioning, sleep, quality of life, and sensory processing. All patients had adverse events. Two patients on everolimus and 2 patients on placebo discontinued treatment due to adverse events.ConclusionsEverolimus did not improve cognitive functioning, autism, or neuropsychological deficits in children with TSC. The use of everolimus in children with TSC with the aim of improving cognitive function and behavior should not be encouraged in this age group.Clinicaltrials.gov identifierNCT01730209.Classification of evidenceThis study provides Class I evidence that for children with TSC, everolimus does not improve intellectual disability, autism, behavioral problems, or other neuropsychological deficits.
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Khozaei, Aida, Hadi Moradi, Reshad Hosseini, Hamidreza Pouretemad, and Bahareh Eskandari. "Early screening of autism spectrum disorder using cry features." PLOS ONE 15, no. 12 (December 10, 2020): e0241690. http://dx.doi.org/10.1371/journal.pone.0241690.
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The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.
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Cohen, Ira L., Xudong Liu, Melissa Hudson, Jennifer Gillis, Rachel N. S. Cavalari, Raymond G. Romanczyk, Bernard Z. Karmel, and Judith M. Gardner. "Level 2 Screening With the PDD Behavior Inventory: Subgroup Profiles and Implications for Differential Diagnosis." Canadian Journal of School Psychology 32, no. 3-4 (August 16, 2017): 299–315. http://dx.doi.org/10.1177/0829573517721127.
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The PDD Behavior Inventory (PDDBI) has recently been shown, in a large multisite study, to discriminate well between autism spectrum disorder (ASD) and other groups when its scores were examined using a machine learning tool, Classification and Regression Trees (CART). Discrimination was good for toddlers, preschoolers, and school-age children; generalized across clinical diagnostic sites; and agreed well with Autism Diagnostic Observation Schedule (ADOS) classifications. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical that differed in developmental history, behavior profiles, and biomedical findings. Seven subtypes of Not-ASD children were identified, two of which were relatively common. Three of the remaining five relatively rare Not-ASD subgroups had highly atypical profiles marked either by extreme aggressiveness or by extreme ritualistic behaviors. PDDBI profiles of these rare subgroups were not previously characterized. In this study, profiles of all CART subgroups based on parent and teacher PDDBIs are described, along with their implications for diagnosis and assessment.
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Skrtic, Thomas M., Argun Saatcioglu, and Austin Nichols. "Disability as Status Competition: The Role of Race in Classifying Children." Socius: Sociological Research for a Dynamic World 7 (January 2021): 237802312110243. http://dx.doi.org/10.1177/23780231211024398.
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Many African American and Hispanic children are classified as mildly disabled. Although this makes special education services available to these and other children who need them, contention endures as to whether disability classification also is racially (and ethnically) biased. The authors view disability classification as status competition, in which minorities are overrepresented in low-status categories such as intellectual disability and emotional disturbance, and whites are overrepresented in high-status categories such as attention-deficit/hyperactivity disorder and autism. The authors address the racialized construction and evolution of the mild disability classification system along with mechanisms that perpetuate racial segmentation in contemporary classification. They analyze a large federal longitudinal data set (1998–2007) to examine racialization and find that classification continues to operate at least in part as a racial sorting scheme. Implications for research and policy are discussed.
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McDuffie, Andrea, Sara Kover, Leonard Abbeduto, Pamela Lewis, and Ted Brown. "Profiles of Receptive and Expressive Language Abilities in Boys With Comorbid Fragile X Syndrome and Autism." American Journal on Intellectual and Developmental Disabilities 117, no. 1 (January 1, 2012): 18–32. http://dx.doi.org/10.1352/1944-7558-117.1.18.
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Abstract The authors examined receptive and expressive language profiles for a group of verbal male children and adolescents who had fragile X syndrome along with varying degrees of autism symptoms. A categorical approach for assigning autism diagnostic classification, based on the combined use of the Autism Diagnostic Interview—Revised and the Autism Diagnostic Observation Schedule (ADOS), and a continuous approach for representing autism symptom severity, based on ADOS severity scores, were used in 2 separate sets of analyses. All analyses controlled for nonverbal IQ and chronological age. Nonverbal IQ accounted for significant variance in all language outcomes with large effect sizes. Results of the categorical analyses failed to reveal an effect of diagnostic group (fragile X syndrome–autism, fragile X syndrome–no autism) on standardized language test performance. Results of the continuous analyses revealed a negative relationship between autism symptom severity and all of the standardized language measures. Implications for representing autism symptoms in fragile X syndrome research are considered.
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Liao, Mengyi, Hengyao Duan, and Guangshuai Wang. "Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder." Journal of Healthcare Engineering 2022 (March 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/9340027.
Повний текст джерелаАнотація:
Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.
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Lahuis, B. E., S. Durston, H. Nederveen, M. Zeegers, S. J. M. C. Palmen, and H. Van Engeland. "MRI-based morphometry in children with multiple complex developmental disorder, a phenotypically defined subtype of pervasive developmental disorder not otherwise specified." Psychological Medicine 38, no. 9 (September 10, 2007): 1361–67. http://dx.doi.org/10.1017/s0033291707001481.
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BackgroundThe DSM-IV-R classification Pervasive Developmental Disorder – Not otherwise Specified (PDD-NOS) is based on the symptoms for autism and includes a wide variety of phenotypes that do not meet full criteria for autism. As such, PDD-NOS is a broad and poorly defined residual category of the autism spectrum disorders. In order to address the heterogeneity in this residual category it may be helpful to define clinical and neurobiological subtypes. Multiple complex developmental disorder (MCDD) may constitute such a subtype. In order to study the neurobiological specificity of MCDD in comparison with other autism spectrum disorders, we investigated brain morphology in children (age 7–15 years) with MCDD compared to children with autism and typically developing controls.MethodStructural MRI measures were compared between 22 high-functioning subjects with MCDD and 21 high-functioning subjects with autism, and 21 matched controls.ResultsSubjects with MCDD showed an enlarged cerebellum and a trend towards larger grey-matter volume compared to control subjects. Compared to subjects with autism, subjects with MCDD had smaller intracranial volume.ConclusionsWe report a pattern of volumetric changes in the brains of subjects with MCDD, similar to that seen in autism. However, no enlargement in head size was found. This suggests that although some of the neurobiological changes associated with MCDD overlap with those in autism, others do not. These neurobiological changes may reflect differences in the developmental trajectories associated with these two subtypes of autism spectrum disorders.
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Samad, A., A. U. Rehman, and S. A. Ali. "Performance Evaluation of Learning Classifiers of Children Emotions using Feature Combinations in the Presence of Noise." Engineering, Technology & Applied Science Research 9, no. 6 (December 1, 2019): 5088–92. http://dx.doi.org/10.48084/etasr.3193.
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Recognition of emotion-based utterances from speech has been produced in a number of languages and utilized in various applications. This paper makes use of the spoken utterances corpus recorded in Urdu with different emotions of normal and special children. In this paper, the performance of learning classifiers is evaluated with prosodic and spectral features. At the same time, their combinations considering children with autism spectrum disorder (ASD) as noise in terms of classification accuracy has also been discussed. The experimental results reveal that the prosodic features show significant classification accuracy in comparison with the spectral features for ASD children with different classifiers, whereas combinations of prosodic features show substantial accuracy for ASD children with J48 and rotation forest classifiers. Pitch and formant express considerable classification accuracy with MFCC and LPCC for special (ASD) children with different classifiers.
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Hayden-Evans, Maya, Benjamin Milbourn, Emily D’Arcy, Angela Chamberlain, Bahareh Afsharnejad, Kiah Evans, Andrew J. O. Whitehouse, Sven Bölte, and Sonya Girdler. "An Evaluation of the Overall Utility of Measures of Functioning Suitable for School-Aged Children on the Autism Spectrum: A Scoping Review." International Journal of Environmental Research and Public Health 19, no. 21 (October 28, 2022): 14114. http://dx.doi.org/10.3390/ijerph192114114.
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A diagnosis of an autism spectrum condition (autism) provides limited information regarding an individual’s level of functioning, information key in determining support and funding needs. Using the framework introduced by Arksey and O’Malley, this scoping review aimed to identify measures of functioning suitable for school-aged children on the autism spectrum and evaluate their overall utility, including content validity against the International Classification of Functioning, Disability and Health (ICF) and the ICF Core Sets for Autism. The overall utility of the 13 included tools was determined using the Outcome Measures Rating Form (OMRF), with the Adaptive Behavior Assessment System (ABAS-3) receiving the highest overall utility rating. Content validity of the tools in relation to the ICF and ICF Core Sets for Autism varied, with few assessment tools including any items linking to Environmental Factors of the ICF. The ABAS-3 had the greatest total number of codes linking to the Comprehensive ICF Core Set for Autism while the Vineland Adaptive Behavior Scales (Vineland-3) had the greatest number of unique codes linking to both the Comprehensive ICF Core Set for Autism and the Brief ICF Core Set for Autism (6–16 years). Measuring functioning of school-aged children on the spectrum can be challenging, however, it is important to accurately capture their abilities to ensure equitable and individualised access to funding and supports.
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Freudenstein, Ornit, Hagit Shimoni, Shahar Gindi, and Yael Leitner. "Disagreement between assessment of ASD utilizing the ADOS-2 and DSM-5 – A preliminary study." Annales Universitatis Paedagogicae Cracoviensis Studia Psychologica 13 (December 30, 2020): 17–26. http://dx.doi.org/10.24917/20845596.13.1.
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Presented study addressed different assessment of ASD obtained with the use of the ADOS-2 and compared with the DSM-5 with children between 8 and 10 years old. Case series data were used on four children who were referred with suspected autism, and as a result a discre- pancy was found between the ADOS-2 assessment and the overall diagnosis.
Initial findings indicated that age, additional diagnoses, and over-reliance on observa- tion may bias the ADOS-2 classification. In particular, children who were diagnosed with other disorders that share symptoms with ASD exhibit behaviors that may bias the ADOS-2 classification as it relies on observed behavior without considering the underlying cause. This discrepancy points to the importance of utilizing and integrating multiple sources of information in the process of establishing an ASD diagnosis, and suggests a need for specialized clinical training in diagnosing autism and other related co-morbid conditions in children aged 8–10.
This preliminary data calls for further research into the area, especially due to the cur- rent over-reliance on the ADOS-2 in clinical practice and research.
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Singh, Akansha, and Surbhi Dewan. "AutisMitr: Emotion Recognition Assistive Tool for Autistic Children." Open Computer Science 10, no. 1 (July 22, 2020): 259–69. http://dx.doi.org/10.1515/comp-2020-0006.
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AbstractAssistive technology has proven to be one of the most significant inventions to aid people with Autism to improve the quality of their lives. In this study, a real-time emotion recognition system for autistic children has been developed. Emotion recognition is implemented by executing three stages: Face identification, Facial Feature extraction, and feature classification. The objective is to frame a system that includes all three stages of emotion recognition activity that executes expeditiously in real time. Thus, Affectiva SDK is implemented in the application. The propound system detects at most 7 facial emotions: anger, disgust, fear, joy, sadness, contempt, and surprise. The purpose for performing this study is to teach emotions to individuals suffering from autism, as they lack the ability to respond appropriately to others emotions. The proposed application was tested with a group of typical children aged 6–14 years, and positive outcomes were achieved.
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Barrios-Fernández, Sabina, Margarita Gozalo, Beatriz Díaz-González, and Andrés García-Gómez. "A Complementary Sensory Tool for Children with Autism Spectrum Disorders." Children 7, no. 11 (November 20, 2020): 244. http://dx.doi.org/10.3390/children7110244.
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Background: Sensory integration (SI) issues are widely described in people with autism spectrum disorder (ASD), impacting in their daily life and occupations. To improve their quality of life and occupational performance, we need to improve clinical and educational evaluation and intervention processes. We aim to develop a tool for measuring SI issues for Spanish children and adolescents with ASD diagnosis, to be used as a complementary tool to complete the Rivière’s Autism Spectrum Inventory, a widely used instrument in Spanish speaking places to describe the severity of ASD symptoms, recently updated with a new sensory scale with three dimensions. Methods: 458 Spanish participants complemented the new questionnaire, initially formed by 73 items with a 1–5 Likert scale. Results: The instrument finally was composed of 41 items grouped in three factors: modulation disorders (13 items), discrimination disorders (13 items), and sensory-based motor disorders (15 items). The goodness-of-fit indices from factor analyses, reliability, and the analysis of the questionnaire’s classification capability offered good values. Conclusions: The new questionnaire shows good psychometric properties and seems to be a good complementary tool to complete new the sensory scale in the Rivière’s Autism Spectrum Inventory.
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Akshoomoff, Natacha, Catherine Lord, Alan J. Lincoln, Rachel Y. Courchesne, Ruth A. Carper, Jeanne Townsend, and Eric Courchesne. "Outcome Classification of Preschool Children With Autism Spectrum Disorders Using MRI Brain Measures." Journal of the American Academy of Child & Adolescent Psychiatry 43, no. 3 (March 2004): 349–57. http://dx.doi.org/10.1097/00004583-200403000-00018.
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Uddin, Lucina Q., Kaustubh Supekar, Charles J. Lynch, Amirah Khouzam, Jennifer Phillips, Carl Feinstein, Srikanth Ryali, and Vinod Menon. "Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism." JAMA Psychiatry 70, no. 8 (August 1, 2013): 869. http://dx.doi.org/10.1001/jamapsychiatry.2013.104.
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Wong, Virginia C. N., Cecilia K. Y. Fung, and Polly T. Y. Wong. "Use of Dysmorphology for Subgroup Classification on Autism Spectrum Disorder in Chinese Children." Journal of Autism and Developmental Disorders 44, no. 1 (May 12, 2013): 9–18. http://dx.doi.org/10.1007/s10803-013-1846-3.
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Capps, Lisa, Marian Sigman, and Peter Mundy. "Attachment security in children with autism." Development and Psychopathology 6, no. 2 (1994): 249–61. http://dx.doi.org/10.1017/s0954579400004569.
Повний текст джерелаАнотація:
AbstractNineteen autistic children were examined in a modified version of Ainsworth's Strange Situation. The attachment security of 15 children could be classified. Each of these children displayed disorganized attachment patterns, but almost half (40%) of them were subclassified as securely attached. To assess the validity of the attachment classifications, children and their mothers were observed in a separate interaction. Mothers of children who were subclassified as securely attached displayed greater sensitivity than mothers of children who were subclassified as insecurely attached. Children who were subclassified as securely attached more frequently initiated social interaction with their mothers than did children who were subclassified as insecurely attached. Children with secure and insecure subclassifications were compared to investigate correlations between attachment organization and representational ability and social-emotional understanding. Although children with underlying secure attachments were no more likely to initiate joint attention, they were more responsive to bids for joint attention, made requests more frequently, and demonstrated greater receptive language ability than children subclassified as insecurely attached. Discussion focuses on dynamics that may contribute to individual differences in the attachment organization of autistic children and on the reciprocal relationship between advances in our understanding of normal and pathological development.
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Erkan, Uğur, and Dang N. H. Thanh. "Autism Spectrum Disorder Detection with Machine Learning Methods." Current Psychiatry Research and Reviews 15, no. 4 (January 15, 2020): 297–308. http://dx.doi.org/10.2174/2666082215666191111121115.
Повний текст джерелаАнотація:
Background: Autistic Spectrum Disorder (ASD) is a disorder associated with genetic and neurological components leading to difficulties in social interaction and communication. According to statistics of WHO, the number of patients diagnosed with ASD is gradually increasing. Most of the current studies focus on clinical diagnosis, data collection and brain images analysis, but do not focus on the diagnosis of ASD based on machine learning. Objective: This study aims to classify ASD data to provide a quick, accessible and easy way to support early diagnosis of ASD. Methods: Three ASD datasets are used for children, adolescences and adults. To classify the ASD data, we used the k-Nearest Neighbours method (kNN), the Support Vector Machine method (SVM) and the Random Forests method (RF). In our experiments, the data was randomly split into training and test sets. The parts of the data were randomly selected 100 times to test the classification methods. Results: The final results were assessed by the average values. It is shown that SVM and RF are effective methods for ASD classification. In particular, the RF method classified the data with an accuracy of 100% for all above datasets. Conclusion: The early diagnosis of ASD is critical. If the number of data samples is large enough, we can achieve a high accuracy for machine learning-based ASD diagnosis. Among three classification methods, RF achieves the best performance for ASD data classification.
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Mendelson, Jenna, Yasmine White, Laura Hans, Richard Adebari, Lorrie Schmid, Jan Riggsbee, Ali Goldsmith, et al. "A Preliminary Investigation of a Specialized Music Therapy Model for Children with Disabilities Delivered in a Classroom Setting." Autism Research and Treatment 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1284790.
Повний текст джерелаАнотація:
Music therapy is gaining popularity as an intervention strategy for children with developmental disabilities, including autism spectrum disorder (ASD). This study was a pilot investigation of a classroom-based music-based intervention, Voices Together®, for improving communication skills in children with ASD and children with intellectual disabilities. Four local public elementary school special education classrooms, serving 5 children with a classification of autistic disorder and 32 children with intellectual disability without autism, were randomly selected to receive one of two levels of exposure to Voices Together music therapy: “long-term” (15 weeks beginning in January 2015 (Time 1), n=14) or “short-term” (7 weeks beginning 7 weeks later in February (Time 2), n=17). Using observational ratings, investigators reliably scored participants live in terms of their level of verbal responsiveness to prompts during three songs featured each week of the program. Both groups demonstrated increases in verbal responses over time; however, only the long-term group demonstrated significant within-group increases. Preliminary findings suggest that music therapy delivered in a classroom in 45-minute weekly sessions for 15 weeks can promote improvements in verbal responsiveness among individuals with autism and other developmental disabilities. Findings warrant further investigation into the efficacy of classroom-based music therapy programs.
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R, Sujatha, Aarthy SL, Jyotir Moy Chatterjee, A. Alaboudi, and NZ Jhanjhi. "A Machine Learning Way to Classify Autism Spectrum Disorder." International Journal of Emerging Technologies in Learning (iJET) 16, no. 06 (March 30, 2021): 182. http://dx.doi.org/10.3991/ijet.v16i06.19559.
Повний текст джерелаАнотація:
In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.
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Tartarisco, Gennaro, Giovanni Cicceri, Davide Di Pietro, Elisa Leonardi, Stefania Aiello, Flavia Marino, Flavia Chiarotti, et al. "Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening." Diagnostics 11, no. 3 (March 22, 2021): 574. http://dx.doi.org/10.3390/diagnostics11030574.
Повний текст джерелаАнотація:
In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.
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Granpeesheh, D., A. Kenzer, and J. Tarbox. "FC05-06 - Comparison of two-year outcomes for children with autism receiving high versus low-intensity behavioral intervention." European Psychiatry 26, S2 (March 2011): 1839. http://dx.doi.org/10.1016/s0924-9338(11)73543-x.
Повний текст джерелаАнотація:
IntroductionBehavioral intervention is an evidence-based treatment for children with autism but there still exists some disagreement regarding how intensive the treatment needs to be. Little previous research has directly compared the effects of high to low-intensity behavioral intervention.ObjectivesTo compare the effects of high versus low-intensity behavioral intervention.AimsCompare outcomes in the area of diagnostic classification, intellectual functioning, executive functions, challenging behavior, language, socialization, and independent living skills after two years of treatment.Methods60 children with autism, under five years old, comprised two groups who received behavioral intervention services. The high-intensity group received 25–35 hours per week for two years and the low-intensity group received 8–15 hours per week of treatment. For all participants, a comprehensive battery of assessments was conducted prior to treatment and at annual intervals.ResultsThe high-intensity group outperformed the low-intensity group on all measures after two years of treatment.ConclusionsThis study provides further evidence that high intensity behavioral intervention produces greater gains than low-intensity treatment and the results suggest that children with autism under the age of five years should receive access to high-intensity treatment