Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Abstract

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare model a reality and has been garnering traction in the field of medicine, with promising results. There have been recent developments in machine learning and/or deep learning algorithms for applications in ophthalmology-primarily for diabetic retinopathy, and age-related macular degeneration. However, AI research in the field of cornea diseases is relatively new. Algorithms have been described to assist clinicians in diagnosis or detection of cornea conditions such as keratoconus, infectious keratitis and dry eye disease. AI may also be used for segmentation and analysis of cornea imaging or tomography as an adjunctive tool. Despite the potential advantages that these new technologies offer, there are challenges that need to be addressed before they can be integrated into clinical practice. In this review, we aim to summarize current literature and provide an update regarding recent advances in AI technologies pertaining to corneal diseases, and its potential future application, in particular pertaining to image analysis.

Keywords: Anterior segment; Artificial intelligence; Cornea; Deep learning; Machine learning.

Fig. 1

Schematic overview of a deep learning (DL) network, in specific convolutional neural network (CNN). a Demonstrated various blocks found in CNN, in specific, convolutional, up sampling and reduction blocks, which are sliding window filters that execute operations on images. b demonstrated the flow of CNN, in particular in this figure, the CNN algorithm would extract features from a raw data input i.e. specular microscopy image, which will then automatically segment the image as a target, producing an edge image as an output, which would then undergo a post-processing directly to produce the final binary segmented image with cell boundary marking