Academic Journal
AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.
| Τίτλος: | AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions. |
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| Συγγραφείς: | Singh K; Department of Computer Science, School of Sciences, Christ University, Bengaluru, 560029, India. kuljeetshan94@gmail.com., Mogha K; Department of Computer Science, School of Sciences, Christ University, Bengaluru, 560029, India., Moctar SMS; Université de Technologie de Compiègne, CNRS, BMBI, Compiègne, France.; CNRS, Université de Rennes, IGDR (Institute Genetics and Development of Rennes), Rennes, France. |
| Πηγή: | Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology [Neurol Sci] 2026 Aug 21; Vol. 47 (9). Date of Electronic Publication: 2026 Aug 21. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer-Verlag Italia Country of Publication: Italy NLM ID: 100959175 Publication Model: Electronic Cited Medium: Internet ISSN: 1590-3478 (Electronic) Linking ISSN: 15901874 NLM ISO Abbreviation: Neurol Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Milano, Italy : Springer-Verlag Italia, c2000- |
| Ιατρικοί όροι (MeSH): | Autism Spectrum Disorder*/diagnostic imaging , Autism Spectrum Disorder*/diagnosis , Neuroimaging*/methods , Neuroimaging*/trends , Artificial Intelligence* , Machine Learning*, Humans |
| Περίληψη: | Background: Autism Spectrum Disorder (ASD) is characterized by challenges in social interaction, communication, and restricted or repetitive behaviors, with motor abnormalities increasingly recognized as potential indicators for early identification. Objective: This paper presents a comprehensive review of recent advancements in ASD research, with particular emphasis on neuroimaging, artificial intelligence (AI), and machine learning (ML)-based diagnostic approaches. The review examines global and country-specific prevalence trends, current diagnostic methodologies, and existing therapeutic interventions. Methods: Through a critical analysis of the literature, including experimental and review studies, key challenges are identified, such as small sample sizes, limited population diversity, heterogeneous imaging protocols, restricted generalizability, and reliance on single-modal datasets. The review further summarizes publicly available ASD datasets and evaluates the strengths and limitations of contemporary AI-driven neuroimaging approaches for ASD diagnosis. Results: The contributions of this study include a comprehensive synthesis of neuroimaging and AI-based diagnostic methods, an analysis of available datasets, and a critical evaluation of current methodological challenges and future research directions. Conclusion: The findings highlight the growing potential of AI-driven tools for supporting early ASD diagnosis while emphasizing the need for standardized protocols, external validation, explainable AI, and clinically translatable frameworks. Future research should focus on improving dataset diversity, conducting multicenter clinical validation studies, and integrating adaptive learning methodologies to enhance the reliability and applicability of ASD diagnostic systems. This study contributes to the growing body of multidisciplinary research aimed at advancing early diagnosis, personalized intervention, and evidence-based clinical decision support for individuals with ASD. (© 2026. Fondazione Società Italiana di Neurologia.) |
| Competing Interests: | Declarations. Conflict of Interest: The authors have no conflicts of interest to declare. Ethical approval and consent to participate: Not applicable. Clinical trial number: Not applicable. Compliance with ethical standards: Not applicable. |
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| Contributed Indexing: | Keywords: Artificial intelligence; Autism Spectrum Disorder (ASD); Early diagnosis; Machine learning; Neuroimaging |
| Entry Date(s): | Date Created: 20260821 Date Completed: 20260821 Latest Revision: 20261012 |
| Update Code: | 20261012 |
| DOI: | 10.1007/s10072-026-09334-4 |
| PMID: | 42625082 |
| Βάση Δεδομένων: | MEDLINE |
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