Developing a New Autism Diagnosis Process Based on a Hybrid Deep Learning Architecture Through Analyzing Home Videos
Valley Christian High School（谷基督教高中）；Lynbrook High School（林布鲁克高中）
Spencer He, Ryan Liu
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11 pages, 3 figures, 4 tables Accepted by International Conference on Artificial Intelligence and Machine Learning for Healthcare Applications (ICAIMLHA 2021)
Currently, every 1 in 54 children have been diagnosed with Autism Spectrum Disorder (ASD), which is 178% higher than it was in 2000. An early diagnosis and treatment can significantly increase the chances of going off the spectrum and making a full recovery. With a multitude of physical and behavioral tests for neurological and communication skills, diagnosing ASD is very complex, subjective, time-consuming, and expensive. We hypothesize that the use of machine learning analysis on facial features and social behavior can speed up the diagnosis of ASD without compromising real-world performance. We propose to develop a hybrid architecture using both categorical data and image data to automate traditional ASD pre-screening, which makes diagnosis a quicker and easier process. We created and tested a Logistic Regression model and a Linear Support Vector Machine for Module 1, which classifies ADOS categorical data. A Convolutional Neural Network and a DenseNet network are used for module 2, which classifies video data. Finally, we combined the best performing models, a Linear SVM and DenseNet, using three data averaging strategies. We used a standard average, weighted based on number of training data, and weighted based on the number of ASD patients in the training data to average the results, thereby increasing accuracy in clinical applications. The results we obtained support our hypothesis. Our novel architecture is able to effectively automate ASD pre-screening with a maximum weighted accuracy of 84%.
Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
University of Pennsylvania（宾夕法尼亚大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Affiliated Shenzhen School of Guangdong Experimental High School（广东实验中学附属深圳学校）；Pazhou Lab（琶洲实验室）
Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Department of Pediatric, Qilu Hospital of Shandong University（山东大学齐鲁医院儿科）；Department of Radiology, Qilu Hospital of Shandong University（山东大学齐鲁医院放射科）；Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院）
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
College of Engineering and Information Technology, University of Dubai, UAE（工程与信息技术学院，迪拜大学）；School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, Australia（电气、计算机与电信工程学院，沃尔longong大学）；College of Medicine and Health Sciences, Mohammed Bin Rashid University Dubai, UAE（医学与健康科学学院，迪拜穆罕默德·本·拉希德大学）
Hybrid Vision Transformer-Mamba Framework for Autism Diagnosis via Eye-Tracking Analysis
Faculty of Science and Technology, Tissemsilt University（科技与技术学院，蒂塞姆西尔大学）；College of Engineering and Information Technology University of Dubai（工程与信息科技学院，迪拜大学）；College of Medicine and Health Sciences, Mohammed Bin Rashid University Dubai, UAE（医学与健康科学学院，迪拜穆罕默德·本·拉希德大学）；School of Clinical Medicine University of New South Wales, Australia（临床医学学院，新南威尔士大学）
Outlier-based Autism Detection using Longitudinal Structural MRI
Amrita School of Engineering（阿姆里塔工程学院）；Amrita Vishwa Vidyapeetham（阿姆里塔大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；University of Canberra（堪培拉大学）
Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder
Southern University of Science and Technology（南方科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Central South University（中南大学）
Detecting Children with Autism Spectrum Disorder based on Script-Centric Behavior Understanding with Emotional Enhancement
School of Computer Science, Wuhan University（武汉大学计算机学院）；Digital Innovation Research Center, Duke Kunshan University（杜克昆山大学数字创新研究中心）；School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子与信息学院）；Child Development and Behavior Center, Third Affiliated Hospital of Sun Yat-sen University（中山大学第三附属医院儿童发展与行为中心）
3D Gait-Based Autism Classification Using Attention-Enhanced Deep Learning with Cross-Fold Statistical Stability Analysis
Begum Rokeya University（贝古姆罗基亚大学）；Texas State University（德克萨斯州立大学）；Asian Institute of Technology（亚洲理工学院）；National Center for Geriatrics and Gerontology（国立长寿医疗研究中心）
Advanced Gesture Recognition for Autism Spectrum Disorder Detection: Integrating YOLOv7, Video Augmentation, and VideoMAE for Naturalistic Video Analysis
Indian Institute of Information Technology, Allahabad（阿拉哈巴德印度信息技术学院）
Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Department of Information and Computer Sciences, University of Hawai‘i at Mānoa（夏威夷大学马诺亚分校信息与计算机科学系）
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
Fujian Agriculture and Forestry University（福建农林大学）；The First Affiliated Hospital of Fujian Medical University（福建医科大学附属第一医院）；Fujian Children's Hospital（福建省儿童医院）
UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning
Wuhan University（武汉大学）；Northeast Normal University（东北师范大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanjing Agricultural University（南京农业大学）；Imperial College London（帝国理工学院）；The Hong Kong University of Science and Technology（香港科技大学）；Harvard University（哈佛大学）
Beyond FACS: Data-driven Facial Expression Dictionaries, with Application to Predicting Autism
Center for Autism Research, The Children’s Hospital of Philadelphia（儿童医院费城医院自闭症研究中心）；Department of Psychiatry, University of Pennsylvania（宾夕法尼亚大学精神病学系）；Deliberate AI
Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework
Shandong University（山东大学）；University of Michigan（密歇根大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
Gaze Behavior During a Long-Term, In-Home, Social Robot Intervention for Children with ASD
Department of Computer Science, Yale University（计算机科学系，耶鲁大学）；Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute（儿童健康、行为与发展中心，西雅图儿童研究研究所）；Department of Pediatrics, University of Washington School of Medicine（儿科系，华盛顿大学医学院）
Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
The University of Nottingham（诺丁汉大学）；Nottingham City Asperger Service & ADHD Clinic（诺丁汉市阿斯伯格服务与多动症诊所）；Institute of Mental Health, The University of Nottingham（诺丁汉大学心理健康研究所）
