Automated quantification of neuropad improves its diagnostic ability in patients with diabetic neuropathy

Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. the diagnostic performance of neuropad was compared between the categorical and continuous (image-analysis (sudometrics)) outputs to diagnose diabetic peripheral neuropathy (dpn). 110 subjects with type 1 and 2 diabetes underwent assessment with neuropad,neuropathy disability score (nds),peroneal motor nerve conduction velocity (pmncv),sural nerve action potential (snap),deep breathing-heart rate variability (db-hrv),intraepidermal nerve fibre density (ienfd),and corneal confocal microscopy (ccm). 46/110 patients had dpn according to the toronto consensus. the continuous output displayed high sensitivity and specificity for db-hrv (91%,83%),cnfd (88%,78%),and snap (88%,83%),whereas the categorical output showed high sensitivity but low specificity. the optimal cut-off points were 90% for the detection of autonomic dysfunction (db-hrv) and 80% for small fibre neuropathy (cnfd). the diagnostic efficacy of the continuous neuropad output for abnormal db-hrv (auc: 91%,p = 0.0003) and cnfd (auc: 82%,p = 0.01) was better than for pmncv (auc: 60%). the categorical output showed no significant difference in diagnostic efficacy for these same measures. an image analysis algorithm generating a continuous output (sudometrics) improved the diagnostic ability of neuropad,particularly in detecting autonomic and small fibre neuropathy. © 2015 georgios ponirakis et al.