检索结果-内蒙古大学图书馆

26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Zhang, Ying Huang, Yaping Qi, Jiansong Zhang, Sihui Tian, Mei Tian, Yi Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China

ISBN: (纸本)9783031439032;9783031439049

People with autism spectrum disorder (asd) show distinguishing preferences for specific visual stimuli compared to typically developed (TD) individuals, opening the door for objective and quantitative screening by eye-tracking data analysis. However, existing eye-tracking-based asd screening approaches often assume that there are no individual differences and that all stimuli contribute equally to the prediction of an asd. Consequently, a fixed number of images are usually selected by a pre-defined strategy for further training and testing, ignoring the distinct characteristics of various subjects viewing the same image. To address the aforementioned difficulties, we propose a novel Uncertainty-inspired asd screening Network (UASN) that dynamically modifies the contribution of each stimulus viewed by different subjects. Specifically, we estimate the uncertainty of each stimulus by considering the variation between the subject's fixation map and the ones of the two clinical groups (i.e., asd and TD) and further utilize it for weighting the training loss. Besides, to reduce the diagnosis time, instead of the shuffle-appeared mode of image viewing, we propose an uncertainty-based personalized diagnosis method to dynamically rank the viewing images according to the preferences of different subjects, which can achieve high prediction accuracy with only a small set of images. Experiments demonstrate the superior performance of our proposed UASN.

关键词： asd screening Eye-tracking Data Uncertainty

来源：评论

学校读者我要写书评

暂无评论

Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills

引用

IEEE ACCESS 2023年 11卷 47907-47929页

作者： Prakash, Varun Ganjigunte Kohli, Manu Kohli, Swati Prathosh, A. P. Wadhera, Tanu Das, Diptanshu Panigrahi, Debasis Kommu, John Vijay Sagar CogniAble Gurugram 122022 Haryana India Indian Inst Sci Dept Elect Commun Engn Signal Proc Bldg West Bengaluru 560012 India Indian Inst Informat Technol Una IIITU Dept Elect & Commun Engn Una 177209 Himachal Prades India Inst Neurodev Kolkata 700005 West Bengal India Jagannath Hosp Bhubaneswar Odisha India Natl Inst Mental Hlth & Neurosci NIMHANS Dept Child & Adolescent Psychiat Bengaluru 560029 Karnataka India

In this paper, the proposed work implements and tests the computer vision applications to perform the skill and emotion assessment of children with Autism Spectrum Disorder (asd) by extracting various bio-behaviors, human activities, child-therapist interactions, and joint pose estimations from the recorded videos of interactive single- or two-person play-based intervention sessions. A comprehensive data set of 300 videos is amassed from asd children engaged in social interaction, and three novel deep learning-based vision models are developed, which are explained as follows: (i) activity comprehension to analyze child-play partner interactions (activity comprehension model);(ii) an automatic joint attention recognition framework using head and hand pose;and (iii) emotion and facial expression recognition. The proposed models are also tested on children's real-world, 68 unseen videos captured from the clinic, and public datasets. The activity comprehension model has an overall accuracy of 72.32%, the joint attention recognition models have an accuracy of 97% for follow eye gaze and 93.4% for hand pointing, and the facial expression recognition model has an overall accuracy of 95.1%. The proposed models could extract behaviors of interest, events of activities, emotions, and social skills from free-play and intervention session videos of long duration and provide temporal plots for session monitoring and assessment, thus empowering clinicians with insightful data useful in diagnosis, assessment, treatment formulation, and monitoring asd children with limited supervision.

关键词： Behavioral sciences Videos Computational modeling Face recognition Autism Variable speed drives Pediatrics Autism spectrum disorder activity comprehension facial expressions joint attention asd screening applied behavior analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing the diagnosis of autism spectrum disorder using phenotypic, structural, and functional MRI data

引用

INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2023年 1-12页

作者： Yakolli, Nivedan Anusha, V. Khan, Adnan Ahamed Shubhashree, A. Chatterjee, Subarna MSRUAS Dept CSE FET Peenya Campus Bengaluru 560058 Karnataka India

Autism spectrum disorder (asd) is a complex and heterogeneous neurodevelopmental disorder. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorder, which is characterized by social deficits and repetitive behaviors. In this paper, we have proposed and implemented a machine learning model and convolution neural network (CNN) for classifying subjects with asd. Data are from Autism Brain Imagining Data Exchange (ABIDE) repository by using phenotypic, sMRI, and fMRI data. For sMRI image dataset, the accuracy of the neural network is about 87%, whereas for fMRI image dataset the accuracy is 88%, which is suitable for real-time usage. We implemented a GUI called Gradio for visualizing the sMRI and fMRI data analysis. The different ML techniques used for phenotypic data were K-nearest neighbors, decision tree, random forest, SVM, and logistic regression. The work also interpreted the different machine learning (ML) models for the clinical data for asd screening of children (toddlers), which are available in the UCI repository;different ML techniques-K-nearest neighbors, decision tree, random forest, and Naive Bayes-are used. The proposed methodology can detect and diagnose asd at early stage. An automated system helps in faster diagnosis, and even minute things are identified and observed. Sometimes, humans can fail in identifying such minute things in the sample while diagnosing. To build such a system, deep learning models such as CNN models are trained on the sMRI and fMRI images to classify them into asd and non-asd. The classification capability of the developed system was measured using the performance metrics such as accuracy, ROC (receiver operating characteristic) curve, and AUC (area under the curve). The automated system can detect whether the given image is asd or normal. Doctors can use this automated system very easily and do the needful. The novelty of our work is that we have considered

关键词： Autism spectrum disorder (asd) ABIDE asd screening Convolution neural network (CNN) Deep learning (DL) GUI Gradio Magnetic resonance imaging (MRI) Machine Learning (ML) Phenotypic data fMRI sMRI

来源：评论

学校读者我要写书评

暂无评论

A new classification system for autism based on machine learning of artificial intelligence

引用

TECHNOLOGY AND HEALTH CARE 2022年第3期30卷 605-622页

作者： Shahamiri, Seyed Reza Thabtah, Fadi Abdelhamid, Neda Univ Auckland Fac Engn Dept Elect Comp & Software Engn Auckland New Zealand ASDTests Auckland New Zealand Abu Dhabi Sch Management Business Analyt Dept Abu Dhabi U Arab Emirates

BACKGROUND: Autistic Spectrum Disorder (asd) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical asd screening techniques are time consuming, so the early detection of asd could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current asd screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs' performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing asd screening to assist the stakeholders in the early identification of asd traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making asd screening more intelligent and accurate.

关键词： Autism asd screening detection systems machine learning medical screening deep neural network

来源：评论

学校读者我要写书评

暂无评论

Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study

引用

JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2020年第1期19卷

作者： Abdelhamid, Neda Padmavathy, Arun Peebles, David Thabtah, Fadi Goulder-Horobin, Daymond Auckland Inst Studies IT Programme Auckland New Zealand Manukau Inst Technol Digital Technol Auckland New Zealand Univ Huddersfield Dept Psychol Huddersfield HD1 3DH W Yorkshire England

Machine learning (ML) is a branch of computer science that is rapidly gaining popularity within the healthcare arena due to its ability to explore large datasets to discover useful patterns that can be interepreted for decision-making and prediction. ML techniques are used for the analysis of clinical parameters and their combinations for prognosis, therapy planning and support and patient management and wellbeing. In this research, we investigate a crucial problem associated with medical applications such as autism spectrum disorder (asd) data imbalances in which cases are far more than just controls in the dataset. In autism diagnosis data, the number of possible instances is linked with one class, i.e. the no asd is larger than the asd, and this may cause performance issues such as models favouring the majority class and undermining the minority class. This research experimentally measures the impact of class imbalance issue on the performance of different classifiers on real autism datasets when various data imbalance approaches are utilised in the pre-processing phase. We employ oversampling techniques, such as Synthetic Minority Oversampling (SMOTE), and undersampling with different classifiers including Naive Bayes, RIPPER, C4.5 and Random Forest to measure the impact of these on the performance of the models derived in terms of area under curve and other metrics. Results pinpoint that oversampling techniques are superior to undersampling techniques, at least for the toddlers' autism dataset that we consider, and suggest that further work should look at incorporating sampling techniques with feature selection to generate models that do not overfit the dataset.

关键词： Autism spectrum disorder asd screening data imbalance machine learning undersampling oversampling SMOTE

来源：评论

学校读者我要写书评

暂无评论

Autism screening Using Deep Embedding Representation 19th

Autism Screening Using Deep Embedding Representation

引用

19th Annual International Conference on Computational Science (ICCS)

作者： Wang, Haishuai Li, Li Chi, Lianhua Zhao, Ziping Fairfield Univ Fairfield CT 06825 USA Harvard Med Sch Boston MA 02115 USA La Trobe Univ Melbourne Vic Australia Tianjin Normal Univ Tianjin Peoples R China

ISBN: (纸本)9783030227418;9783030227401

Autism spectrum disorder (asd) is a developmental disorder that affects communication and behavior. An early diagnosis of neurodevelopmental disorders can improve treatment and significantly decrease associated healthcare cost, which reveals an urgent need for the development of asd screening. However, the data used for asd screening is heterogenous and multi-source, resulting in existing screening tools for asd screening are expensive, time-intensive and sometimes fall short in predictive accuracy. In this paper, we apply novel feature engineering and feature encoding techniques, along with a deep learning classifier for asd screening. Algorithms were created via a robust deep learning classifier and deep embedding representation for categorical variables to diagnose asd based on behavioral features and individual characteristics. The proposed algorithm is effective compared with baselines, achieving 99% sensitivity and 99% specificity. The results suggest that deep embedding representation learning is a reliable method for asd screening.

关键词： Autism spectrum disorder Deep learning asd screening Categorical embedding

来源：评论

学校读者我要写书评

暂无评论

screening Accuracy of the Brief Infant Toddler Social-Emotional Assessment to Identify Autism Spectrum Disorder in Toddlers Born at Less Than 30 Weeks' Gestation

引用

CHILD PSYCHIATRY & HUMAN DEVELOPMENT 2018年第4期49卷 493-504页

作者： Boone, Kelly M. Brown, Anne K. Keim, Sarah A. Nationwide Childrens Hosp Res Inst Ctr Biobehav Hlth Columbus OH 43205 USA Ohio State Univ Crane Ctr Early Childhood Res & Policy 175 East 7th Ave Columbus OH 43201 USA Ohio State Univ Schoenbaum Family Ctr 175 East 7th Ave Columbus OH 43201 USA Ohio State Univ Coll Med Dept Pediat Columbus OH 43210 USA Ohio State Univ Div Epidemiol Coll Publ Hlth Columbus OH 43210 USA

Despite the higher prevalence of autism spectrum disorder (asd) in children born preterm, valid screening tools for use in preterm populations are lacking. We aimed to evaluate the screening accuracy of the Brief Infant Toddler Social-Emotional Assessment (BITSEA) and to compare it to the Pervasive Developmental Disorders screening Test-II, Stage 2, Developmental Clinic Screener (PDDST-II-DCS) in identifying asd diagnosis in toddlers born at < 30 weeks' gestation. Caregivers (94% mothers) of 555 children completed questionnaires (BITSEA, PDDST-II-DCS, socio-demographics) when the children (58% male) were 18-36 months. Medical charts were abstracted 3.5 years later and showed that 4% (n = 24) of children had an asd diagnosis. BITSEA competence (sensitivity = .74;specificity = .76) and asd (sensitivity = .70;specificity = .73) subscales demonstrated better accuracy in identifying asd compared to the recommended PDDST-II-DCS cut-score (sensitivity = .73;specificity = .64), specifically as it related to specificity. Additional studies are needed in other preterm populations to replicate these findings.

关键词： Prematurity Autism spectrum disorder BITSEA asd screening

来源：评论

学校读者我要写书评

暂无评论

Combining parent and clinician ratings of behavioural indicators of autism spectrum disorder improves diagnostic classification

引用

EARLY CHILD DEVELOPMENT AND CARE 2018年第6期188卷 748-758页

作者： Ward, Samantha L. Sullivan, Karen A. Gilmore, Linda Queensland Univ Technol Sch Psychol & Counselling Kelvin Grove Australia Stepping Stones Life Psychol QLD Pty Ltd Brisbane Qld Australia Queensland Univ Technol Inst Hlth & Biomed Innovat Brisbane Qld Australia Queensland Univ Technol Sch Cultural & Profess Learning Kelvin Grove Qld Australia Queensland Univ Technol Children & Youth Res Ctr Brisbane Qld Australia

Reliance on a single informant method in the evaluation of autism spectrum disorder (asd) can increase misclassification. Yet, current asd screening instruments predominately rely on a single informant approach. This study combined scores from two asd screening instruments, the clinician-administered Autistic Behavioural Indicators Instrument (ABII) and its Parent Questionnaire (ABII-PQ) equivalent, to examine whether a combination score improved asd classification in a sample of children with asd (n = 51, M-child age=3.6 years, SD = 1.01, range = 2-6 years). Results showed the ABII and ABII-PQ were significantly associated, r =.62, p = .01, and did not differ significantly on final correct asd classification. However, final agreement on asd classification between instruments was poor, K =.19, P = .184. One method of combining scores from the ABII and ABII-PQ significantly improved classification, correctly classifying 100% of children with asd. Results show combining scores from parent- and clinician-rated screening instruments could improve asd classification.

关键词： Autism spectrum disorder asd asd screening ABII ABII-PQ

来源：评论

学校读者我要写书评

暂无评论

New Assessments and Treatments in asd

引用

CURRENT TREATMENT OPTIONS IN NEUROLOGY 2017年第2期19卷 6-6页

作者： Choueiri, Roula N. Zimmerman, Andrew W. Univ Massachusetts Childrens Mem Hosp Div Dev & Behav Pediat Worcester MA 01605 USA Univ Massachusetts Childrens Mem Hosp Div Child Neurol Worcester MA 01605 USA

The assessment of autism spectrum disorder (asd) is complex and remains clinical, despite advances in basic research. In this chapter, we review new and updated clinical tools, such as screening and diagnostic tests, and discuss the DSM-5 criteria introduced in 2013. We provide an algorithm to guide clinical evaluation and referrals. We also review nonbehavioral treatments and summarize recent research. Current conventional treatment of asd in children includes intensive behavioral interventions (known as applied behavioral analysis or ABA), rehabilitative services such as speech therapy, occupational therapy, physical therapy, social skills training, and counseling. We present new validated information and provide clinical guidance for the evaluation and treatment of young children and youth with asd.

关键词： asd Assessment New treatments Updates asd screening Drug therapy

来源：评论

学校读者我要写书评

暂无评论

Examination of the Korean Modified Checklist of Autism in Toddlers: Item Response Theory

引用

JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2015年第9期45卷 2744-2757页

作者： Seung, HyeKyeung Ji, Juye Kim, Soo-Jin Sung, Inkyung Youn, Young-Ah Hong, Gyunghun Lee, Hyeonjin Lee, Young Hwan Lee, Hyunsuk Youm, Hyun Kyung Calif State Univ Fullerton Dept Human Commun Studies Fullerton CA 92831 USA Calif State Univ Fullerton Dept Social Work Fullerton CA 92831 USA Korea Nazarene Univ Dept Commun Disorders Cheonan South Korea Catholic Univ Korea Dept Pediat Seoul South Korea Yeungnam Univ Dept Early Childhood Educ Gyongsan South Korea Yeungnam Univ Dept Pediat Gyongsan South Korea Childrens World Clin Seoul South Korea Ewha Womans Univ Dept Educ Seoul South Korea

The study examined the clinical utility and psychometric properties of the Korean Modified Checklist of Autism in Toddlers (K-M-CHAT)-2. A sample of 2300 parents of 16- to 36-month-old children was recruited across South Korea. A phone interview was utilized to follow up with participants who initially screened positive for autism spectrum disorder (asd). Item response theory was applied to assess the psychometric properties of the K-M-CHAT-2. Parents' responses were substantially changed after the follow-up, and the final screen-positive rate was 2.3 %. Results indicated that the psychometric properties of items 1, 3, 11, 18 and 22 were not as strong as the other items. The K-M-CHAT-2 is a useful asd screening test when implemented with a follow-up.

关键词： asd screening Korean M-CHAT Item response theory Population screening

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：