Jan 2026· Heliyon· 4 citations· ⚡ 1 influential· 90 references
Computer Science
TL;DR
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Abstract
Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.
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
Autism spectrum disorder (ASD), a neurodevelopmental condition, affects approximately 1% of children globally and their social and cognitive abilities. This leads to difficulties in communication, repetitive behaviors, psychomotor skills, and eye contact maintenance. In recent years, there has been an increased utilization of Artificial Intelligence (AI) for the early detection of autism. Using knowledge gathered from 116 peer-reviewed publications, this study assessed algorithmic efficacy, model performance, multimodal data integration, classification metrics, generalization ability, and clinical usefulness. Machine Learning, Deep Learning (DL), Graph Neural Networks, Federated Learning, auto encoders, and Natural Language Processing, the Attention Mechanism seemed to be among the several AI techniques that were investigated in this study. To protect a child's developmental progress, this survey investigates how early detection of autism facilitates prompt therapeutic treatments, minimizes intellectual disabilities, improves mobility, and supports customized care. The research emphasizes the effectiveness of predicting ASD with minimal time using different data modalities, including EEG microstates, ABIDE I & II, to assess DL models such as GoogleNet, Xception, AlexNet, ResNet, VGG, and DenseNet. Three types of data were analyzed: biochemical (biomarkers and physiological metrics), behavioral (facial features, eye gazing, and audio-video cues), and structural and functional (MRI, EEG, and ECG) images. The research demonstrated strong diagnostic performance with models attaining accuracy rates ranging from 90% to 96% across diverse datasets. Classification measures, such as accuracy, sensitivity, specificity, precision, recall, and F1-score, were used in the performance evaluation. Error and statistical metrics, such as RMSE, MSE, R2, Kappa, and G-mean, were also employed. The dependability and efficiency of the models in detecting ASD were enhanced by validation methods, such as confusion matrix, receiver operating characteristics (ROC) curves, and AUC. Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis. Even with a minimal data size, these techniques increase robustness, accuracy, and generalization. For real-time clinical applications, 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
Early identification of Autism Spectrum Disorder (ASD) is crucial for enabling timely intervention and improving developmental outcomes. Conventional diagnostic methods rely heavily on behavioral observation and expert judgment, often leading to delayed diagnosis. In recent years, machine learning and deep learning techniques have been increasingly explored to support early ASD prediction using facial images, behavioral videos, neuro imaging data, and clinical datasets. This review presents a comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive performance, and key limitations. The findings indicate that deep learning-based approaches, particularly those using facial images and behavioral video analysis, generally achieve higher accuracy compared to traditional machine learning models. However, challenges such as limited dataset diversity, reduced cross-dataset generalization, lack of interpretability, and insufficient clinical validation remain prevalent across studies. In addition to the review, this work includes the implementation of an existing deep learning-based facial image classification method to validate reported findings. Overall, this critical review highlights current progress and emphasizes the need for robust, interpretable, and clinically validated approaches to support early ASD screening and complement traditional diagnostic practices.
Gaurangkumar Patel, H.B. Pandya, B. Trivedi· SPU - Journal of Science, Te...· 0 citations
Background Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition affecting social interaction, communication, and behavior, with traditional diagnosis relying on subjective and time-consuming behavioral assessments. Advances in neuroimaging have enhanced understanding of the brain mechanisms underlying ASD. Objective This systematic review aimed to comprehensively examine ASD classification datasets and recent advancements in ASD diagnosis using neuroimaging modalities, and to analyze machine learning techniques for ASD diagnosis to evaluate their diagnostic performance in terms of accuracy and Area Under the Curve (AUC). Methods The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive literature search (2021–2025) was conducted across major databases, including Web of Science, IEEE Xplore, ACM, ScienceDirect, MDPI, and Springer. Results Out of 2,329 initially identified records, 825 were screened for eligibility after title and abstract review. The final analysis included 107 studies, which predominantly used structural and functional Magnetic Resonance Imaging, Electroencephalography, and multimodal datasets for ASD classification. The most common classifiers were Convolutional Neural Networks, Support Vector Machines, Random Forests, and hybrid Deep Learning (DL) models. Studies reported performance metrics such as accuracy and AUC, with many showing promising diagnostic results. Key limitations included small sample sizes, lack of external validation, dataset imbalance, and limited generalizability across multi-site datasets. Conclusion Neuroimaging-based Machine Learning (ML) offers strong potential for improving ASD diagnosis but faces challenges in reproducibility, interpretability, dataset variability, and clinical translation. Future work should focus on multi-site validation, explainable AI, and standardized evaluation to ensure reliable, real-world applications.
N. Ahmed, Ayesha Tajammul, Afzal Badshah et al.· Digital Health· 0 citations
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a broad spectrum of symptoms, which makes timely and accurate diagnosis challenging. The development of machine learning (ML) and deep learning (DL) has created opportunities for automated ASD screening and detection. This systematic review focuses on the analyses of 59 peer‐reviewed studies on unimodal and multimodal approaches to ASD detection that were published between 2019 and 2025. The results demonstrated that classical ML algorithms (such as logistic regression [LR], support vector machines [SVM] and random forests [RF]) and DL models (convolutional neural networks [CNN], recurrent neural networks (RNN) and transformers) were used to assess the accuracy of the diagnosis for a variety of data modalities ranging from behavioural measures to neuroimaging, electroencephalography (EEG), eye tracking and speech, with accuracy from 68% to 99%. A careful examination of these studies, however, shows that they share certain common flaws, including small sample size, demographic bias, overfitting and absence of external validation. Hybrid multimodal frameworks have been shown to yield consistent performance improvements over unimodal frameworks, with accuracies of 95%–99% achieved through attention, graph‐based learning and hybrid fusion approaches. This review highlights four major points: (1) a critical review of dataset ethics and validity, even for non‐clinical facial image datasets; (2) an architectural comparison of multimodal fusion strategies (early fusion, late fusion and hybrid fusion) focusing on computational complexity and clinical applicability; (3) a quantitative summarization of the performance trends by modalities and sample size; and (4) a structured review of indicators of reproducibility and regulatory hurdles for clinical translation. This review suggests the need to develop large, well‐balanced datasets, the application of explainable AI (XAI) techniques, standardization (e.g., brain imaging data structure [BIDS]) and regulatory guidelines for facilitating the clinical translation of ASD detection systems.
Anupama N, Chandrashekar M Patil· International Journal of Dev...· 0 citations
