Artificial intelligence-based classification of diabetic peripheral neuropathy from corneal confocal microscopy images

Abstract

Diabetic peripheral neuropathy (DPN) is characterized by pain and sensory loss, affecting approximately 50% of patients. Early identification and risk factor management are key to limiting progression of DPN. In contrast to retinopathy (retinal fundus imaging) and nephropathy (microalbuminuria) with early disease detection, the 10-g monofilament identifies advanced DPN. Corneal confocal microscopy (CCM) is an ophthalmic imaging technique that identifies subclinical corneal nerve loss, which predicts incident DPN and has good diagnostic utility for DPN. It also identifies corneal nerve regeneration prior to improvement in symptoms and nerve conduction studies after simultaneous pancreas and kidney transplantation. CCM studies have primarily used manual corneal nerve analysis (CCMetrics), which, although highly reliable, is time-consuming with limited scalability.