Mahmoud Elbattah ^1* Osman Ali Sadek Ibrahim ² Gilles Dequen ³

• ¹ University of the West of England, Bristol, United Kingdom
• ² Minia University, Minya, Minya, Egypt
• ³ University of Picardie Jules Verne, Amiens, Picardy, France

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterised by challenges in social communication, repetitive behaviours, and restricted interests [1]. Early and accurate diagnosis is critical for effective intervention, enabling individuals with ASD to achieve better developmental outcomes and an improved quality of life. However, traditional diagnostic methods, often reliant on subjective behavioural observations, remain timeintensive and inconsistently accessible. This underscores an urgent need for innovative, scalable, and objective diagnostic tools [2,3].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 utilised to quantify gaze behaviours 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 [3,7]. These technological advancements provide a foundation for developing tools that are not only efficient but also potentially generalisable across diverse populations.Furthermore, approaches such as transforming gaze scanpaths into visual representations for classification have simplified the diagnostic pipeline, enabling the automation of traditionally laborious processes [4]. Additionally, unsupervised learning techniques, including clustering of eye-tracking data, have demonstrated potential for extracting unique insights into the variability of ASD presentations [5]. These developments illustrate the growing synergy between AI-driven tools and clinical practices.Beyond eye tracking, other data modalities such as structural MRI (sMRI), resting-state functional connectivity (rsFC), and multimodal approaches integrating genetic, behavioural, 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 [2,6]. Despite these promising innovations, challenges persist. Standardisation of methodologies, reproducibility of results, and the translation of research into clinical applicability remain significant barriers. This special issue seeks to address these challenges by presenting cuttingedge 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 issue explore various aspects of ASD diagnosis through ML, presenting innovative approaches and significant findings: Eslami et al. provide a comprehensive review of ML models applied to sMRI and fMRI datasets, examining their efficacy in diagnosing ASD and related disorders. The study highlights key advancements in deep learning architectures and identifies limitations such as data heterogeneity and reproducibility challenges. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2020.575999 Vector Machine (SVM) models. Their research uncovers discriminative connectivity patterns within the Default Mode Network (DMN), achieving high classification accuracy and reinforcing the potential of rsFC data in ASD diagnostics. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.761942 Jia et al. conduct a bibliometric analysis, mapping the global research landscape of AI applications in ASD. Their findings highlight trends such as the rise of feature selection and the significance of multimodal integration, providing a roadmap for future studies. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2023.1310400 Ruan et al. present an exploratory study on using micro-expressions as diagnostic biomarkers. Despite the challenges posed by video data quality, their work emphasises the need for multimodal approaches combining behavioural and neuroimaging data. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1435091 The contributions in this issue emphasise the multidimensional nature of ASD and the need for a holistic diagnostic framework. Challenges such as the lack of standardisation 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 personalised treatment strategies and early intervention protocols.We envision that future research should focus on:• Data Diversity and Multimodal Integration: Combining imaging, genetic, and behavioural data to enhance model robustness.• Interpretable AI: Developing transparent algorithms that clinicians can trust and use effectively.