{"title":"自闭症谱系障碍早期发现与严重程度评估的影像与问卷混合方法","authors":"Rajkumar S.C. , Stefano Cirillo , Yuvasini D. , Luisa Solimando","doi":"10.1016/j.imavis.2025.105547","DOIUrl":null,"url":null,"abstract":"<div><div>In this research, we propose a novel integrated system for the early diagnosis and cognitive enhancement of infants with Autism Spectrum Disorder (ASD). The system combines two core modules: the Behavioral Analytic Module and the Cognitive Skill Enhancement Module. The Behavioral Analytic Module includes a Questionnaire Analysis Sub-module, which utilizes Random Forest classifiers to analyze questionnaire data, and an Image Analysis Sub-module, which employs a fine-tuned VGG16 Convolutional Neural Network to process facial images. These sub-modules independently assess ASD likelihood and combine their outputs to generate a comprehensive diagnosis using a weighted averaging technique. The Cognitive Skill Enhancement Module integrates interactive games and web-based animations designed to improve cognitive abilities and daily living skills in toddlers with ASD. Additionally, a reward system is incorporated to reinforcement learning outcomes, adaptively calculating rewards based on the infants’ progress. The proposed system aims to provide a holistic approach to ASD diagnosis and intervention, offering an effective tool for early detection and tailored cognitive development. The system’s efficacy is demonstrated through comparative analysis, showing a 93% improvement in diagnostic accuracy and a 92% enhancement in cognitive skill development among toddlers with ASD.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"160 ","pages":"Article 105547"},"PeriodicalIF":4.2000,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid approach combining images and questionnaires for early detection and severity assessment of Autism Spectrum Disorder\",\"authors\":\"Rajkumar S.C. , Stefano Cirillo , Yuvasini D. , Luisa Solimando\",\"doi\":\"10.1016/j.imavis.2025.105547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this research, we propose a novel integrated system for the early diagnosis and cognitive enhancement of infants with Autism Spectrum Disorder (ASD). The system combines two core modules: the Behavioral Analytic Module and the Cognitive Skill Enhancement Module. The Behavioral Analytic Module includes a Questionnaire Analysis Sub-module, which utilizes Random Forest classifiers to analyze questionnaire data, and an Image Analysis Sub-module, which employs a fine-tuned VGG16 Convolutional Neural Network to process facial images. These sub-modules independently assess ASD likelihood and combine their outputs to generate a comprehensive diagnosis using a weighted averaging technique. The Cognitive Skill Enhancement Module integrates interactive games and web-based animations designed to improve cognitive abilities and daily living skills in toddlers with ASD. Additionally, a reward system is incorporated to reinforcement learning outcomes, adaptively calculating rewards based on the infants’ progress. The proposed system aims to provide a holistic approach to ASD diagnosis and intervention, offering an effective tool for early detection and tailored cognitive development. The system’s efficacy is demonstrated through comparative analysis, showing a 93% improvement in diagnostic accuracy and a 92% enhancement in cognitive skill development among toddlers with ASD.</div></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":\"160 \",\"pages\":\"Article 105547\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885625001350\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885625001350","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A hybrid approach combining images and questionnaires for early detection and severity assessment of Autism Spectrum Disorder
In this research, we propose a novel integrated system for the early diagnosis and cognitive enhancement of infants with Autism Spectrum Disorder (ASD). The system combines two core modules: the Behavioral Analytic Module and the Cognitive Skill Enhancement Module. The Behavioral Analytic Module includes a Questionnaire Analysis Sub-module, which utilizes Random Forest classifiers to analyze questionnaire data, and an Image Analysis Sub-module, which employs a fine-tuned VGG16 Convolutional Neural Network to process facial images. These sub-modules independently assess ASD likelihood and combine their outputs to generate a comprehensive diagnosis using a weighted averaging technique. The Cognitive Skill Enhancement Module integrates interactive games and web-based animations designed to improve cognitive abilities and daily living skills in toddlers with ASD. Additionally, a reward system is incorporated to reinforcement learning outcomes, adaptively calculating rewards based on the infants’ progress. The proposed system aims to provide a holistic approach to ASD diagnosis and intervention, offering an effective tool for early detection and tailored cognitive development. The system’s efficacy is demonstrated through comparative analysis, showing a 93% improvement in diagnostic accuracy and a 92% enhancement in cognitive skill development among toddlers with ASD.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.