Keratoconus Classifier for Smartphone-based Corneal Topographer
This work addresses the need for accessible keratoconus screening in underserved areas, particularly the Global South, by automating diagnosis from smartphone-based devices.
The researchers tackled the problem of diagnosing keratoconus using low-cost smartphone-based corneal topographers by developing a dual-head CNN classifier, achieving a sensitivity of 91.3% and specificity of 94.2%.
Keratoconus is a severe eye disease that leads to deformation of the cornea. It impacts people aged 10-25 years and is the leading cause of blindness in that demography. Corneal topography is the gold standard for keratoconus diagnosis. It is a non-invasive process performed using expensive and bulky medical devices called corneal topographers. This makes it inaccessible to large populations, especially in the Global South. Low-cost smartphone-based corneal topographers, such as SmartKC, have been proposed to make keratoconus diagnosis accessible. Similar to medical-grade topographers, SmartKC outputs curvature heatmaps and quantitative metrics that need to be evaluated by doctors for keratoconus diagnosis. An automatic scheme for evaluation of these heatmaps and quantitative values can play a crucial role in screening keratoconus in areas where doctors are not available. In this work, we propose a dual-head convolutional neural network (CNN) for classifying keratoconus on the heatmaps generated by SmartKC. Since SmartKC is a new device and only had a small dataset (114 samples), we developed a 2-stage transfer learning strategy -- using historical data collected from a medical-grade topographer and a subset of SmartKC data -- to satisfactorily train our network. This, combined with our domain-specific data augmentations, achieved a sensitivity of 91.3% and a specificity of 94.2%.