Edited and reviewed by: Michael Denker, Jülich Research Centre, Germany
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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests (American Psychiatric Association,
Machine Learning (ML) has emerged as a transformative approach to ASD diagnosis, offering the ability to analyse large, complex datasets and uncover patterns that surpass human capability. For instance, eye-tracking technologies have been extensively utilized to quantify gaze behaviors such as fixations and saccades, as well-established markers of autism. Studies employing Deep Learning have achieved high accuracy in classifying ASD from typically developing individuals based on eye-tracking data (Jeyarani and Senthilkumar,
Furthermore, approaches such as transforming gaze scanpaths into visual representations for classification have simplified the diagnostic pipeline, enabling the automation of traditionally laborious processes (Carette et al.,
Beyond eye tracking, other data modalities such as structural MRI (sMRI), resting-state functional connectivity (rsFC), and multimodal approaches integrating genetic, behavioral, and imaging data have shown promise in identifying robust biomarkers for ASD. These diverse methodologies underscore the importance of leveraging multidimensional data to improve diagnostic precision and reliability (Rasul et al.,
Despite these promising innovations, challenges persist. Standardization of methodologies, reproducibility of results, and the translation of research into clinical applicability remain significant barriers. This Research Topic seeks to address these challenges by presenting cutting-edge research that integrates ML and neuroinformatics to enhance the accuracy, efficiency, and accessibility of ASD diagnostics. By bridging the gap between technology and practice, this collection of studies aims to drive the field toward more effective and equitable solutions for ASD diagnosis.
The articles included in this Research Topic explore various aspects of ASD diagnosis through ML, presenting innovative approaches and significant findings:
The contributions in this Research Topic emphasize the multidimensional nature of ASD and the need for a holistic diagnostic framework. Challenges such as the lack of standardization across datasets, ethical considerations in algorithm deployment, and the interpretability of ML models remain relevant. However, the integration of advanced computational methods with clinical expertise opens avenues for personalized treatment strategies and early intervention protocols.
We envision that future research should focus on:
ME: Writing – original draft, Writing – review & editing. OA: Writing – review & editing. GD: Writing – review & editing.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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