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

Abstract

Background: 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. Methods: 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. Findings: 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. Interpretation: 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.

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

“…A transfer net 'inceptionResNet' convolutional neural network from Matlab was used with retinal images as input to develop the classification models. The method was discussed in a previous study by Lai et al 12 First, features based on pixels associated with diabetes status were generated. We also extracted the texture-related features, fractal-related features such as fractal dimension, and spectrum-related features such as high-order spectra associated with diabetes using the ARIA algorithm.…”

“…We have previous experience developing the methodology and validating the results in other disease cohorts, including stroke, coronary heart disease, cerebral small vessel disease, and autism spectrum disorder. [12][13][14][15][16] After the classification model was developed, we used a 10-fold cross-validation method based on a support vector machine (SVM) algorithm to evaluate the performance of the model and to minimize potential bias in the modeling process. We first partitioned the data set and used a subset with 90% data to train the algorithm and the remaining 10% data for testing.…”

Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders

“…Finally, we translated the features extracted from the machine learning approaches to commonly used retinal characteristics measured from the images using ImageJ to gain further insights. We have previously applied this method and validated results in different disease cohorts [ 56 , 57 , 58 ].…”

Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model

“…Prior work using natural language processing and machine learning to extract social risk information from clinical notes of adult patients in the United States has been effective [ 14 - 17 ]; however, there is limited use of this work within pediatric settings. The use of machine learning-based algorithms in pediatric medicine has been explored to optimize detection/diagnosis, treatment, and outcome/risk predictions in children who suffer from specific conditions such as severe sepsis [ 18 ], autism spectrum disorder [ 19 - 22 ], traumatic brain injuries [ 23 ], substance use disorder [ 24 ], and asthma [ 25 ]. The benefits and drawbacks of their usage in pediatric clinical care have been described by others [ 26 , 27 ].…”