Machine Learning Models for Early Autism Spectrum Disorder Detection: Systematic Review With a Pediatric and Engineering Perspective

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition for which earlier identification may facilitate timely developmental assessment and intervention. This systematic review synthesized evidence on machine learning (ML)/ deep learning (DL) approaches for early ASD detection, with particular attention to pediatric populations, data modalities, model architectures, reported diagnostic performance, validation strategies, methodological quality, and clinical and engineering translation. PubMed and IEEE Xplore were searched through July 29, 2026. The supplied database exports contained 794 PubMed records and 455 IEEE Xplore records. After removal of seven records published after the prespecified cutoff, 1242 records remained. Thirty-eight duplicates were removed, leaving 1204 unique records for screening. Following title/abstract screening, 314 reports underwent detailed record-level eligibility assessment, of which 237 were classified as record-level eligible on the basis of available titles, abstracts, bibliographic metadata, and accessible record information. The identified evidence encompassed questionnaire and clinical-information models, home-video and behavioral analysis, speech and acoustic features, eye tracking, EEG/MEG, MRI/fMRI, wearable sensors, and multimodal approaches. Reported diagnostic performance varied substantially across populations, modalities, algorithms, and validation settings. Some retrospective studies reported very high classification accuracy, whereas independent and cross-cultural validation generally produced more variable performance. For example, an ML model based on medical and background information reported an AUROC of 0.895 during development and 0.790 in independent validation, while a real-world evaluation of an AI-based diagnostic system reported sensitivity of 99.1% and specificity of 81.6% among determinate outputs. Because the available evidence was heterogeneous and full-text reports were not available for all candidate studies, quantitative pooling and definitive study-level risk-of-bias assessment were not considered defensible. PROBAST+AI domains were therefore assessed only when sufficient methodological information was available, with otherwise unclear or not-assessable judgments. Across the evidence base, external validation, prevention of data leakage, dataset shift, explainability, privacy, fairness, reproducibility, computational requirements, and clinical workflow integration remain major barriers to translation. ML and DL approaches show promise as adjunctive screening and risk-classification technologies, but the available evidence does not support replacing specialist ASD assessment with autonomous AI diagnosis. Future research should prioritize prospective multicenter validation, transparent reporting, independent testing, clinically meaningful explainability, privacy-preserving methods, health-equity evaluation, calibration, and deployment within real-world pediatric clinical workflows.