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 (1). 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 (2) and has good diagnostic utility for DPN (3). It also identifies corneal nerve regeneration prior to improvement in symptoms and nerve conduction studies after simultaneous pancreas and kidney transplantation (4). CCM studies have primarily used manual corneal nerve analysis (CCMetrics), which, although highly reliable, is time-consuming with limited scalability. Here, we combine a deep learning (DL) algorithm for fully automated quantification of corneal nerves in CCM images along with an adaptive neuro-fuzzy inference system (ANFIS) to rapidly differentiate patients with (DPN+) and without (DPN−) neuropathy and healthy control subjects. Participants with type 1 diabetes ( n = 87) and control subjects ( n = 21) underwent detailed assessment of neuropathy (Table 1). Based on the Toronto criteria, which combine symptoms, signs, and abnormal nerve conduction, patients were subdivided into DPN+ (29%) and DPN− (71%) groups (Table 1). Participants underwent CCM, and 6–8 central corneal nerve images/subject were quantified using our established methodology (5 …