Prediction of causative genes in inherited retinal disorders from spectral‐domain optical coherence tomography utilizing deep learning techniques

Abstract

AbstractTechnological advancement has markedly increased the medical information required for clinical management. Processing medical information automatically and comprehensively through the application of artificial intelligence (AI) is becoming essential to improve the quality of medical data management and reduce medical expenses.Deep learning techniques have been successfully applied in medical fields, especially in analysing clinical images for the predicted diagnosis of common ocular disorders, such as diabetic retinopathy and age‐related macular degeneration. In contrast, due to limited clinical resources, AI‐oriented bioinformatic engineering has not yet been applied in a clinic for orphan diseases, such as inherited retinal disease (IRD), although IRD is the most prevalent cause of blindness.Therefore, the purpose of the current study was to investigate the utility of deep learning in a nationwide cohort of patients with IRD, which enables to explore the typical phenotypes of IRD based on unique findings from clinical images and predict the genetic diagnosis.Clinical and genetic data with IRD were reviewed. Three categories of genetic diagnosis were selected based on the high prevalence of their causative genes: Stargardt disease (ABCA4), retinitis pigmentosa (EYS), and occult macular dystrophy (RP1L1). Optical coherence tomographic (OCT) images were cropped in a standardized manner with a macro algorithm. Algorithms for pipeline analyses, based on TensorFlow Inception V‐3 were determined with learning parameters through evaluation with a randomized 4‐fold cross‐validation method, given the accuracy of concordance (aimed >80%) between the genetic diagnosis and the machine diagnosis (ABCA4, EYS, RP1L1, and normal).A total of 178 images from 75 subjects were examined. The mean training accuracy was 98.5%, ranging from 90.6 to 100.0. The mean overall test accuracy was 90.9% (82.0–97.6).This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD‐OCT, with a high prediction accuracy. The obtained study results have been registered in the database to support clinical/genetic diagnosis. These achievements will extensively promote the improvement of medical care quality by providing accurate real‐time diagnoses, reducing cost by avoiding unnecessary referrals and testing, enriching the education of non‐specialists, and realizing an application of personalized medicine.