Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system

Abstract

PurposeWe 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). MethodsWe developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images from four hospitals. It identi es 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 ve-fold cross-validation approach to generate the classi cation results. ResultsSeveral 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 classi cation 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. ConclusionsIRIDS identi es 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 arti cial intelligence integration into ophthalmology to achieve better outcomes in PPPM services.

Multi-risk factors joint prediction model for risk prediction of retinopathy of prematurity

“…Low-risk preterm infants should receive fewer examinations to alleviate scarce medical resources, while high-risk infants (premature infants with ROP who require treatment) should be treated with medication or laser surgical interventions to reduce the risk of blindness, which effectively promotes the paradigm shift from reactive medicine to the advanced approach by utilizing the 3 PM framework. The active promotion of the PPPM framework has been applied in many fields, making great contributions to the health quality improvement including fundus diseases [ 32 ], diabetes [ 33 ], cancers [ 34 ], etc. In this study, we retrospectively evaluated an AI system for ROP risk and TP-ROP risk prediction developed for preterm infants in Chinese population based on the i-ROP cohort study dataset from SZEH.…”