Automated Cataracts Screening from Slit-Lamp Images Employing Deep Learning

Abstract

To assess the feasibility and performance using deep learning networks to automatically detect cataracts from slit-lamp images in large-scale eye diseases screening scenarios. Two datasets were collected using, respectively, the professional Slit-Lamp Microscopes (SLM) and the portable Slit-Lamp Devices (SLD) clipped on a Smartphone, during routine eye disease screening programs in China. The former Dataset-M comprised 4891 images from 1670 subjects and the latter Dataset-D comprised 2516 images from 802 subjects. Each image was then labelled by three ophthalmologists as one of the three classes: 1) un-gradable image, 2) cataract, and 3) normal. For each dataset, two deep learning models were created: one for image quality assessment, and the other for cataracts detection, and the performance of which was assessed by the Area Under a ROC Curve (AUC) and kappa agreement. For the quality assessment models, on Dataset-M (Dataset-D), the corresponding AUC achieved were 0.929 (0.881), with kappa agreements of 0.628 (0.590) and p < 0.001, respectively. For the cataract detection models, the corresponding AUC were 0.997 (0.987), with kappa agreements of 0.912 (0.893) and p < 0.001, respectively. Furthermore, based on these models we built a practical cloud application that has been trialled in 25 real-world screening settings in China, receiving favourable feedbacks from clinicians, primary care physicians and patients alike.