The article reviews 55 machine‑learning studies on autism spectrum disorder (ASD) published between 2017 and 2023, focusing on how ML can aid early diagnosis and treatment. It finds that supervised learning dominates current research, while deep learning is gaining traction as data volumes grow. The review highlights the need for models that fuse complex data—such as genetic, clinical, wearable, and biometric sources—to improve diagnostic accuracy and enable continuous, non‑intrusive monitoring.
arXiv:2306.14300v2 Announce Type: replace-cross Abstract: Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and...
arXiv:2607. 07957v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited.
arXiv:2412. 06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role.
arXiv:2609.14159v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments...
arXiv:2609.16464v1 Announce Type: cross Abstract: The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and co...
AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.
arXiv:2607. 08533v1 Announce Type: new Abstract: Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative.
arXiv:2608. 07567v1 Announce Type: cross Abstract: Functional near-infrared spectroscopy (fNIRS) is a promising modality for autism spectrum disorder (ASD) classification, yet existing approaches assume temporally aligned evaluation.
arXiv:2608. 07393v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well.
arXiv:2607. 28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs.
arXiv:2511. 05221v3 Announce Type: replace Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $\alpha$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy.
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.