Aug 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1125-1128· 0 citations
TL;DR
This project introduces a smart, hybrid system that combines advanced deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help for autism, by blending advanced computational analysis with trusted treatment practices.
Abstract
With Early detection of Autism Spectrum Disorder (ASD) can make a life-changing difference in a child’s journey,
helping them receive the right support at the right time. This project introduces a smart, hybrid system that combines advanced
deep learning technology with proven treatment methods, aiming to close the gap between diagnosis and meaningful help. It
examines various types of information such as behavioural assessments and sensory response patterns to train a model that can
identify early signs of autism with high accuracy and consistency. When the system detects a possible case, it provides structured,
theory-based activities designed to develop cognitive, social, emotional, and communication skills in young children. These
activities are based on widely accepted approaches and are intended to encourage steady developmental growth. A major strength
of this system is its automated reporting feature, which gathers diagnostic insights, structured treatment recommendations, and
predicted progress into a clear, easy-to-read report for parents, therapists, and healthcare professionals, ensuring everyone stays
informed and aligned. By blending advanced computational analysis with trusted treatment practices, the system ensures both
accurate detection and a smooth path to intervention. It supports early diagnosis, ongoing guidance, and continuous monitoring,
helping reduce delays and improving engagement in a child’s developmental plan. This combined approach demonstrates how
technology and professional expertise can work together to create an accessible, practical tool for ASD management. Its goal is
to transform early detection into immediate, meaningful action that nurtures potential, builds confidence, and helps shape a
brighter future for every child.
This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences, highlighting the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.
Muhamad Syukron, R. Faresta· Jurnal Ilmiah Kursor· 0 citations
Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.
Devireddy Mamatha, K. Maheswari· 2026 6th International Confe...· 0 citations
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.
S. K, Lakshmi Annapurna Y· Journal of Visualized Experi...· 0 citations
Artificial intelligence shows promise as a supportive tool for early screening, but current evidence supports its use as a complement to, rather than replacement for, clinical assessment.
Andrea Catalina Mahecha Ballesteros, Juanita Valeria García Bello, Eleaine Scarlet González Zuñiga et al.· Current Psychiatry Reports· 0 citations
The proposed ensemble-based machine learning classifier methodology presented in this study seeks to revolutionize the diagnosis of ASD by harnessing the collective power of various machine learning algorithms to enhance diagnostic precision, mitigate the subjectivity associated with traditional diagnostic methods, and accelerate the detection process.
Shabeena Lylath, Laxmi B. Rananavare· IAES International Journal o...· 0 citations
A Hybrid Intelligent Model designed to predict ASD in pediatric cases, leveraging adaptive neuro-fuzzy systems integrates artificial neural network capabilities with fuzzy logic, offering a comprehensive approach to ASD prediction.
Nneka MaryAnn Okafor, C. Ituma, R. Nweze· Communication in Physical Sc...· 0 citations
