Jul 2026· Journal of Visualized Experiments· Vol 233· 0 citations
Medicine
Abstract
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.
The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
Anupama N, Chandrashekar M. Patil· International Journal of Dev...· 0 citations
A comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive performance, and key limitations indicates that deep learning-based approaches, particularly those using facial images and behavioral video analysis, generally achieve higher accuracy than traditional machine learning models.
Gaurangkumar Patel, H.B. Pandya, B. Trivedi· SPU - Journal of Science, Te...· 0 citations
Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis, and a hybrid transfer learning-based framework is advised to assist clinicians and therapists in accurately diagnosing and assessing ASD severity.
R. Thillaikarasi, P. Kumaresan· International Conference on...· 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
Conventional diagnostic tests for Autism Spectrum Disorder (ASD) involve the use of subjective behavioral observations and questionnaires completed by the clinician, which can be time-consuming and subjectto human bias. The challenge encourages the development of innovative, data-driven methods to facilitateearly and accurate identification of ASD. The research proposes a Multi-Model Ensemble Approach Using Deep and traditional learning for ASD classification (MME-ASD) model. The MME-ASD model encompassesthree traditional machine learning (ML) and two deep learning (DL) algorithms that perform according to a weighted majority voting strategy. The five learning paradigms are Random Forest (RF), Decision Tree (DT), Neural Networks(NN), Convolutional Neural Networks(CNN), and Deep Recurrent Neural Networks(DRNN),which are utilized to enhance classification accuracy and generalization. An ensemble evaluation method is proposed to complete this study andassess the efficiency of the proposed MME-ASD model. The MME-ASD model acquires complementary properties by using numeric and textual data from a publicly available dataset of ASD, which includes information on 704 adults, both with and without a diagnosis. Initially, during the evaluation phase, the performance of the standalone traditional ML and DL algorithms was assessed acrossseveral train-test ratios. Subsequently, the proposed MME-ASD ensemble was evaluated with a 60-40 split to ensure compatibility with the baseline models. Finally, a 3-fold cross-validation experiment was conducted to assess the robustness and generalization of the proposed MME-ASD model. The experimental outcomes reveal that the MME-ASD model outperformsindividual learners for both cross-validation and train-test assessments. It records evaluation metrics of accuracy 99.57%,precision 99.48%, and recall 98.94% across the 3-fold cross-validation experiments. The findings verify that incorporating deep and traditional learning models in an ensemble framework can significantly enhance the classification of ASD, offering a dependable and scalable computationalmodel to aid clinical specialists in the initial diagnosis of ASD.
Dhafar Fakhry Hasan, Maha A. Abdul-Jabar, Mawadah Mohammed Suliman et al.· Journal of Soft Computing an...· 0 citations