Deep learning-driven approach for cataract management: towards precise identification and predictive analytics

Abstract

Deep learning (DL) technology has shown significant potential in the whole process of cataract diagnosis and treatment through algorithms such as convolutional neural network (CNN). In terms of diagnosis, DL models based on fundus or slit-lamp images can automatically identify and grade cataract, and their diagnostic accuracy is close to or beyond the level of human experts. In the field of surgery, DL can analyze the operation video stage in real time, accurately track the instruments and optimize the operation process, and reduce the risk of intraoperative eye error through intelligent devices. DL could optimize the intraocular lens (IOL) power calculation, predict the risk of complications and long-term surgery requirements. However, insufficient data standardization, the “black box” characteristics of the model, and privacy ethics issues are still the bottlenecks in clinical application. In the future, it is necessary to improve the generalization ability of model through multimodal data fusion, federated learning and other technologies, and combine interpretable design (such as Grad-CAM) to promote the evolution of DL to a transparent medical decision-making tool, and finally realize the intelligence and universality of cataract management.