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

Georgios Ponirakis, Hassan Fadavi, Ioannis N. Petropoulos, Shazli Azmi, Maryam Ferdousi, Mohammad A. Dabbah, Ahmad Kheyami, Uazman Alam, Omar Asghar, Andrew Marshall, Mitra Tavakoli, Ahmed Al-Ahmar, Saad Javed, Maria Jeziorska, Rayaz Malik

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10 Citations (Scopus)


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.

Original languageEnglish
Article number847854
JournalJournal of Diabetes Research
Publication statusPublished - 2015


ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

Cite this

Ponirakis, G., Fadavi, H., Petropoulos, I. N., Azmi, S., Ferdousi, M., Dabbah, M. A., Kheyami, A., Alam, U., Asghar, O., Marshall, A., Tavakoli, M., Al-Ahmar, A., Javed, S., Jeziorska, M., & Malik, R. (2015). Automated quantification of neuropad improves its diagnostic ability in patients with diabetic neuropathy. Journal of Diabetes Research, 2015, [847854].