ArticleEClinicalMedicine2020
A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder.
Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06306378 (The Involvement of Sleep Spindle Waves in the Auxiliary Diagnosis of Social Memory Disorders in Children With Autism Spectrum Disorders), which is not on this map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The Involvement of Sleep Spindle Waves in the Auxiliary Diagnosis of Social Memory Disorders in Children With Autism Spectrum Disorders
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Oculomics approaches using retinal imaging to predict mental health disorders: a systematic review and meta-analysis.International ophthalmology · 2025Pooled it
- Review
- A contrastive reconstruction vision transformer model for intelligent diagnosis of autism spectrum disorder.NPJ digital medicine · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Screening autism spectrum disorder in children using machine learning on speech transcripts.Scientific reports · 2025Article
- Disease Diagnosis Using Retinal Vasculature: Insights from Flammer Syndrome and AI.Brain sciences · 2025Review
- Deep learning diagnosis plus kinematic severity assessments of neurodivergent disorders.Scientific reports · 2025Article
- Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation.International journal of molecular sciences · 2025Review
- A Window to the Brain-The Enduring Impact of Vision Research.Brain sciences · 2025Review
- Ocular manifestations in autism spectrum disorder.International journal of ophthalmology · 2025Review
- Review
- Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs.JAMA network open · 2023Article
- ASDHeliyon · 2023Article
- The Physiology of Cognition in Autism Spectrum Disorder: Current and Future Challenges.Cureus · 2023Review
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder.Brain sciences · 2023Article
- Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies.Frontiers in neuroscience · 2023Review
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis.BMJ open diabetes research & care · 2022Article
- Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy.
methodsForty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity.
findingsThe sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants.
interpretationBecause ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.
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