Automated Detection of Nine Infantile Fundus Diseases and Conditions in Retinal Images Using a Deep Learning System

Abstract

Abstract Purpose We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and conditions due to a shortage of ophthalmologists. Our aim is to provide personalized monitoring and early intervention to prevent complications associated with infantile fundus diseases, aligning with predictive, preventive, and personalized medicine (PPPM).Methods We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images from four hospitals. It identifies nine fundus diseases and includes depth attention modules, Res-18, and MaxViT. Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results.Results Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with values of 90.25%, 87.69%, 83.38%, 85.48%, 83.51, and 98.04%, respectively. In comparison, IRIDS outperformed the ophthalmologists with an average accuracy, precision, recall, F1, kappa, and AUC of 96.45%, 95.86%, 94.37%, 95.03%, 94.43, and 99.51%, respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. Compared with ophthalmologists, the IRIDS achieved a higher AUC for the detection of retinal abnormalities.Conclusions IRIDS identifies nine infantile fundus diseases and conditions accurately. It can aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in PPPM services.