Automated Cornea Diagnosis using Deep Convolutional Neural Networks based on Cornea Topography Maps

Abstract

Cornea topography maps allow ophthalmologists to screen and diagnose cornea pathologies. We aimto automatically identify any cornea abnormalitiesbased on such cornea topography maps, with focus on diagnosing keratoconus. A set of 1946 consecutive screening scans from the Saarland University Hospital Clinic for Ophthalmology was annotated and used for model training and validation. All scans were recorded witha CASIA2 anterior segment Optical Coherence Tomography (OCT) scanner.We propose to represent the OCT scans as images and apply Convolutional Neural Networks (CNNs) for the automatic analysis. The developed model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. On a new dataset, our model achieves a sensitivity of 97% and a specificity of 97% when distinguishing between Healthy and Pathological corneas. While a comparison to previous work is intricate due tosignificant variations in the experimental setup, our model outperforms other published studies, either in terms of detection performance, and/or in terms of number of potential cornea abnormalities the model can identify. Furthermore, the proposed approach is independent of the topography scanner and allows to visually represent scan regions that drive the models' decision.

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

“…Furthermore, a public data set enables DL scientists worldwide to test new techniques to advance the accuracy of predictive analytics. Finally, it will be important to update the systematic review including newly published evidence [32][33][34][35][36][37] to capture progress in model development and to monitor reporting quality and guideline adherence.…”