ArticleBrain communications2026
Machine learning-based skin nerve morphometry for diabetic neuropathy: diagnostic and clinical implications.
Article in Brain communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aimed to (i) develop and validate new intraepidermal nerve fibres (IENFs) biomarkers with the aid of machine learning algorithms for the diagnosis of small-fibre neuropathy in diabetic patients and (ii) explore the diagnostic performance and clinical significance of these new biomarkers. Patients with diabetic neuropathy and control subjects were recruited. Area-based morphometry of IENF (IENFa) parameters were developed by using the machine learning system for automatic quantification. The diagnostic performance was assessed according to receiver operating characteristic analysis. The clinical implications of the various IENFa parameters were examined by exploring their correlations with metabolic profiles and via electrophysiological experiments. The diabetic neuropathy (
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