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.
Highlights
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterised by impairments in social interaction and communication that begin at a young age, which commonly manifest as repetitive behaviours and restricted interests [1]
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that begin at a young age and it affects many aspects of the individual’s life including social interaction, communication and the way sensory information was being processed
We extracted the texture/fractal/spectrum related features that are associated with ASD by using the automatic retinal image analysis (ARIA) algorithm written in Matlab [33]
Summary
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterised by impairments in social interaction and communication that begin at a young age, which commonly manifest as repetitive behaviours and restricted interests [1]. Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that begin at a young age and it affects many aspects of the individual’s life including social interaction, communication and the way sensory information was being processed. Previous studies have shown that thinning of retinal nerve fibre layer was significantly associated with high functioning ASD and Asperger Syndrome. It suggested that retinal image may provide critical information for the classification of ASD. 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. Further diagnosis and confirmation can be made by professionals, and potential treatment may be provided at a relatively early stage
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