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REVIEW article

Front. Microbiol.

Sec. Systems Microbiology

Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1492484

Age of Machine Learning: New Trends in Autism Spectrum Disorder Prediction

Provisionally accepted
  • Department of Military Medical Sciences Academy, Academy of Military Sciences, Tianjin, China

The final, formatted version of the article will be published soon.

In recent years, there has been an increase in the incidence of autism spectrum disorder (ASD), its pathogenesis remains unknown, and there are no effective treatments available. Early identification of individuals at risk enables early targeted intervention, which improves outcomes. Through the integration of artificial intelligence and the medical field, researchers can establish a machine learning (ML) risk prediction model to estimate the risk of ASD. Currently, a variety of risk models have been developed using multiple factors, such as genetic background, gaze behavior, adverse conditions during pregnancy and childbirth, magnetic resonance imaging of the brain, and intestinal microbial composition, to predict ASD. These ML prediction models have shown some reliability in predicting ASD risk. In the future, ML prediction models for ASD will present significant challenges and opportunities, potentially helping identify drug targets for developing novel therapies to alleviate ASD symptoms and enable precision medicine.

Keywords: Autism Spectrum Disorder, machine learning, Risk prediction model, Intestinal Microbiome, biomarkers

Received: 07 Sep 2024; Accepted: 12 Jun 2025.

Copyright: © 2025 Xu, Li, Li and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Min Jin, Department of Military Medical Sciences Academy, Academy of Military Sciences, Tianjin, China

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