Prediction of the risk of diabetic foot from corneal nerve images using deep learning algorithms

We aimed to develop a deep learning algorithm (DLA) for predicting the risk categories of diabetic foot using corneal nerve images.

A total of 23,550 images from 942 eyes of 471 participants were included. We first developed a DLA based on corneal nerve images alone. We then combined classic clinical risk factors of diabetic peripheral neuropathy and quantitative corneal nerve parameters to develop hybrid DLAs. Model performances were assessed based on the area under the receiver operating characteristic curve (AUC).

For the DLA using corneal nerve images alone, the AUC was 0.69 for the prediction of diabetic foot and 0.76 for the identification of patients at high risk. For the hybrid DLAs, the algorithm achieved an AUC of 0.94 for the prediction of diabetic foot with the only addition of HbA1c, and an AUC of 0.93 for the identification of patients with high-risk diabetic foot with the incorporation of serum creatinine. When using quantitative corneal nerve parameters, the performance was not improved compared to using corneal nerve images alone.

Our DLAs integrating corneal nerve images with HbA1c or serum creatinine have good performance in predicting and stratifying diabetic foot risk, providing a new screening approach.