ArticleDiagnostics (Basel, Switzerland)2022
Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies.Journal of autism and developmental disorders · 2026Pooled it
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- Improving Autism Diagnosis Across Ages Using Eye-Tracking and Temporal Transformer Models.Sensors (Basel, Switzerland) · 2026Article
- Participant-Independent Classification of Autism-Related Visual Attention Patterns from Eye-Tracking Scanpath Images Using a Global-Local Fusion Network.Journal of eye movement research · 2026Article
- Home-based eye tracking for early autism screening: a scoping review of approaches, evidence, and implementation challenges.BMC psychiatry · 2026Article
- MACAFNet transformer-based multi-atlas fusion framework for autism spectrum disorder classification using functional connectivity.Scientific reports · 2026Article
- Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches.Molecular brain · 2025Review
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Distinguishing Dyslexia, Attention Deficit, and Learning Disorders: Insights from AI and Eye Movements.Bioengineering (Basel, Switzerland) · 2025Article
- Physicochemical and Sensory Evaluation of Sustainable Plant-Based Homopolymers as an Alternative to Traditional Emollients in Topical Emulsions.Pharmaceutics · 2025Article
- DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms.Frontiers in physiology · 2025Article
- Diagnosing autism spectrum disorder based on eye tracking technology using deep learning models.Frontiers in medicine · 2025Article
- Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology.Diagnostics (Basel, Switzerland) · 2024Article
- A novel multi-modal model to assist the diagnosis of autism spectrum disorder using eye-tracking data.Health information science and systems · 2024Article
- [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
- Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study.JMIR research protocols · 2024Article
- Article
- Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis.Frontiers in medicine · 2024Article
- A review of machine learning in scanpath analysis for passive gaze-based interaction.Frontiers in artificial intelligence · 2024Review
- Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.Biomolecules · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder is a group of disorders marked by difficulties with social skills, repetitive activities, speech, and nonverbal communication. Deficits in paying attention to, and processing, social stimuli are common for children with autism spectrum disorders. It is uncertain whether eye-tracking technologies can assist in establishing an early biomarker of autism based on the children's atypical visual preference patterns. In this study, we used machine learning methods to test the applicability of eye-tracking data in children to aid in the early screening of autism. We looked into the effectiveness of various machine learning techniques to discover the best model for predicting autism using visualized eye-tracking scan path images. We adopted three traditional machine learning models and a deep neural network classifier to run experimental trials. This study employed a publicly available dataset of 547 graphical eye-tracking scan paths from 328 typically developing and 219 autistic children. We used image augmentation to populate the dataset to prevent the model from overfitting. The deep neural network model outperformed typical machine learning approaches on the populated dataset, with 97% AUC, 93.28% sensitivity, 91.38% specificity, 94.46% NPV, and 90.06% PPV (fivefold cross-validated). The findings strongly suggest that eye-tracking data help clinicians for a quick and reliable autism screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.