Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in which children with ASD react to visual stimulation in a controlled environment have been developed to help facilitate early diagnosis. Previous studies have shown that, when exposed to a video that presents both social and abstract scenes side by side, a child with ASD will focus their attention towards the abstract images on the screen to a greater extent than a child without ASD. Such differential responses make it possible to implement an algorithm for the rapid diagnosis of ASD based on eye tracking against different visual stimuli. Here we propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons and employing a different camera than the one used during testing. In addition to this, the proposed algorithm achieves a fast response time, providing a near real-time evaluation of ASD. Thereby, by applying the proposed method, we could significantly reduce the diagnosis time and facilitate the diagnosis of ASD in low resource regions.
IIT Delhi（德里印度理工学院）；Florida Institute of Technology（佛罗里达理工学院）
Diagnostic and intervention methodologies for skill assessment of autism typically requires a clinician repetitively initiating several stimuli and recording the child's response. In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos. However, supervised learning of neural networks demand large amounts of annotated data that are hard to come by. This issue is addressed by leveraging the `similarities' between the action categories in publicly available large-scale vide…
Autism often remains undiagnosed in adolescents and adults. Prior research has indicated that an autistic individual often shows atypical fixation and gaze patterns. In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an au…
Universidad Peruana Cayetano Heredia（秘鲁卡耶塔诺·埃雷迪亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Autism is a developmental disorder that affects social interaction and communication of children. The gold standard diagnostic tools are very difficult to use and time consuming. However, diagnostic could be deduced from child gaze preferences by looking a video with social and abstract scenes. In this work, we propose an algorithm based on convolutional neural networks to predict gaze direction for a fast and effective autism diagnosis. Early results show that our algorithm achieves real-time response and robust high accuracy for prediction of gaze direction.
This paper has been withdrawn by the authors due to insufficient or definition error(s) in the ethics approval protocol. Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms and an accurate diagnosis. In this work, we contribute a new database consisting of video clips of typical (normal) and atypical (such as hand flapping, spinning or rocking) behaviors, displayed in natural settings, which have been…
Santa Clara University（圣克拉拉大学）；University of Missouri（密苏里大学）
In this paper, we introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Although the disease is considered to be genetic, the highest rates of accurate diagnosis occur when the child is tested on behavioral characteristics and facial features. Patients have a common pattern of distinct facial deformities, allowing researchers to analyze only an image of the child to determine if the child has the dis…
With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis. Many of these children struggle to make eye contact, recognize facial expressions, and engage in social interactions. Today the standard for treatment of the core autism-related deficits focuses on a form of behavior training known as Applied Behavioral Analysis. To address perceived deficits in expression recognition, ABA approaches routinely involve the use of prompts such as flash cards for repetitive emotion recognition training…
Autism spectrum disorder (ASD) is a developmental disorder that influences the communication and social behavior of a person in a way that those in the spectrum have difficulty in perceiving other people's facial expressions, as well as presenting and communicating emotions and affect via their own faces and bodies. Some efforts have been made to predict and improve children with ASD's affect states in play therapy, a common method to improve children's social skills via play and games. However, many previous works only used pre-trained models on benchmark emotion datasets and failed to consid…
A Hybrid Deep Learning Framework for Emotion Recognition in Children with Autism During NAO Robot-Mediated Interaction
Department of Information Technology, School of Engineering, Cochin University of Science and Technology（科钦大学科学与技术学院信息科技系）；Indian Institute of Technology Kanpur（印度理工学院坎普尔学院）
Technische Universität München（慕尼黑工业大学）；University of Cambridge（剑桥大学）
Individuals with Autism Spectrum Conditions (ASC) have marked difficulties using verbal and non-verbal communication for social interaction. The running ASC-Inclusion project aims to help children with ASC by allowing them to learn how emotions can be expressed and recognised via playing games in a virtual world. The platform includes analysis of users' gestures, facial, and vocal expressions using standard microphone and web-cam or a depth sensor, training through games, text communication with peers, animation, video and audio clips. We present the state of play in realising such a serious g…
Improving Autism Detection with Multimodal Behavioral Analysis
Center for Cognitive Interaction Technology (CITEC), Bielefeld University（认知交互技术中心（CITEC），比勒菲尔德大学）；Institute of Psychology, Humboldt University of Berlin（心理学研究所，洪堡大学）；Department of Psychiatry and Psychotherapy, Medical Center-University of Freiburg（精神病学与心理治疗系，弗赖堡医学院-大学）
Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Key Laboratory of Intelligent Bioinformatics（深圳市智能生物信息学重点实验室）；Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院高性能计算中心）
Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models
Coventry University（考文垂大学）；University of Memphis（孟菲斯大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；University of Nevada Las vegas（内华达大学拉斯维加斯分校）；Softwarica College（索夫瓦里卡学院）；GEICO（盖可保险公司）；Growphase Technology（格罗法斯科技）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Curry College（库里学院）
Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder
Faculty of Artificial Intelligence in Education, Central China Normal University（华中师范大学教育人工智能学院）；Computer Science and Artificial Intelligence School, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）
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）
Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized …
Exploring Gaze Pattern Differences Between Autistic and Neurotypical Children: Clustering, Visualisation, and Prediction
Singapore University of Technology and Design（新加坡科技设计大学）；A*STAR Institute for Infocomm Research（新加坡资讯通信研究院）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）
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）
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）
AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Shenzhen Maternity and Child Healthcare Hospital Affiliated to Southern Medical University（南方医科大学附属深圳市妇幼保健院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
University of Southampton（南安普顿大学）；IRCCS Stella Maris Foundation（IRCCS斯特拉马里斯基金会）
Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful…
Stanford University（斯坦福大学）；University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）
A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies which detect the presence of behaviors related to autism can scale access to pediatric diagnoses. This work aims to demonstrate the feasibility of deep learning technologies for detecting hand flapping from unstructured home videos as a first step towards validating whether models and digital technologies can be leveraged to aid with autism diagnose…
Yale School of Medicine（耶鲁医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Yale University（耶鲁大学）；George Washington University（乔治·华盛顿大学）
Treating children with autism spectrum disorders (ASD) with behavioral interventions, such as Pivotal Response Treatment (PRT), has shown promise in recent studies. However, deciding which therapy is best for a given patient is largely by trial and error, and choosing an ineffective intervention results in loss of valuable treatment time. We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy. Our proposed learning pipeline uses random forest regression to determine candidate brain voxels that may be inf…
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder
University of Southern California（南加州大学）；Korea University（高丽大学）；Boston University（波士顿大学）；University of California San Francisco（加州大学旧金山分校）；University of California Los Angeles（加州大学洛杉矶分校）
A multimodal large language model for evidence-based autism spectrum disorder screening
Zhejiang Normal University（浙江师范大学）；State Grid Corporation of China（国家电网有限公司）；Huzhou Normal University（湖州师范学院）；Zhejiang University of Technology（浙江工业大学）
Indian Institute of Technology Bombay（印度理工学院孟买分校）；University of Reading（雷丁大学）；De Montfort University（德蒙福特大学）；Sangath（桑加斯）
Screening for any of the Autism Spectrum Disorders is a complicated process often involving a hybrid of behavioural observations and questionnaire based tests. Typically carried out in a controlled setting, this process requires trained clinicians or psychiatrists for such assessments. Riding on the wave of technical advancement in mobile platforms, several attempts have been made at incorporating such assessments on mobile and tablet devices. In this paper we analyse videos generated using one such screening test. This paper reports the first use of the efficacy of using the observer's distan…
The University of Tokyo（东京大学）；NTT Corporation（日本电信电话株式会社）
The existing computational visual attention systems have focused on the objective to basically simulate and understand the concept of visual attention system in adults. Consequently, the impact of observer's age in scene viewing behavior has rarely been considered. This study quantitatively analyzed the age-related differences in gaze landings during scene viewing for three different class of images: naturals, man-made, and fractals. Observer's of different age-group have shown different scene viewing tendencies independent to the class of the image viewed. Several interesting observations are…
Coimbatore Institute of Technology（哥印拜陀理工学院）；Steps Rehabilitation Center (I), Steps groups（Steps康复中心（I），Steps集团）
Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic fa…
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph repres…
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks
Kennesaw State University（肯尼索州立大学）；University of Calabria（卡拉布里亚大学）
本文针对自闭症图像分类任务，采用多种迁移学习神经网络模型比较 CPU 与 GPU 的性能，结果表明 GPU 在执行时间和准确率上均优于 CPU。
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）
Athena Research and Innovation Center（雅典娜研究与创新中心）；National Technical Univ. of Athens（雅典国立技术大学）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Institute of Intelligent Systems and Robotics（智能系统与机器人研究所）
In this work we tackle the problem of child engagement estimation while children freely interact with a robot in their room. We propose a deep-based multi-view solution that takes advantage of recent developments in human pose detection. We extract the child's pose from different RGB-D cameras placed elegantly in the room, fuse the results and feed them to a deep neural network trained for classifying engagement levels. The deep network contains a recurrent layer, in order to exploit the rich temporal information contained in the pose data. The resulting method outperforms a number of baseline…
Valley Christian High School（谷基督教高中）；Lynbrook High School（林布鲁克高中）
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…
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）
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）
Deep Learning Based Approach to Enhanced Recognition of Emotions and Behavioral Patterns of Autistic Children
Department of Information Technology（信息科技系）；Sri Lanka Institute of Information Technology（斯里兰卡信息科技研究所）；Department of Computer Systems Engineering（计算机系统工程系）
Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals, Artists, and Neurotypical Observers
Université de Tours（图尔大学）；Université d’Orléans（奥尔良大学）；Université Sorbonne Paris Nord（索邦巴黎北大学）
