A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder

• Published in: EClinicalMedicine Vol. 28; p. 100588
• Main Authors: Lai, Maria, Lee, Jack, Chiu, Sally, Charm, Jessie, So, Wing Yee, Yuen, Fung Ping, Kwok, Chloe, Tsoi, Jasmine, Lin, Yuqi, Zee, Benny
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2020; Elsevier
• Subjects: Autism spectrum disorder; Automatic retinal image analysis; Internal Medicine; Machine learning; Research Paper; Risk assessment; screening tool; screening tool; Automatic retinal image analysis; Risk assessment; Autism spectrum disorder; Machine learning
• ISSN: 2589-5370; 2589-5370
• DOI: 10.1016/j.eclinm.2020.100588

Autism 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. Forty-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. The 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. Because 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.