Machine learning-based skin nerve morphometry for diabetic neuropathy: diagnostic and clinical implications.

Overview

1. Department of Neurology, National Taiwan University Hospital, Taipei 100225, Taiwan
2. Department of Anatomy and Cell Biology, National Taiwan University College of Medicine, Taipei 100225, Taiwan
3. Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu 302058, Taiwan
4. School of Medicine, National Taiwan University, Taipei 100225, Taiwan
5. Department of Neurology, National Taiwan University Hospital Yunlin Branch, Yunlin 640, Taiwan
6. Department of Medical Education and Research, National Taiwan University Hospital Yun-Lin Branch, Douliu 640, Taiwan
7. Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 106319, Taiwan

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 (n = 48) and control (n = 63) cohorts were comparable in terms of age and sex. The IENFa parameters were inversely correlated with age, and only the IENF density (IENFd, the number of fibres per unit length of epidermis) and IENFa/A parameters were observed to be sex dependent in the control group. All of the IENFa parameters demonstrated equivalent performance according to (i) the correlation with IENFd and (ii) the diagnosis of IENFd-based small-fibre neuropathy by the receiver operating characteristic analysis (area under curve: 0.91–0.95, P > 0.05). Furthermore, the IENFa biomarkers were significantly correlated with sural sensory nerve action potential amplitudes. In summary, automatic IENFa is time-efficient and performs comparably to IENFd in diagnosing diabetic small-fibre neuropathy with high reliability. Furthermore, the IENFa parameter reflects concurrent large-fibre involvement in diabetic neuropathy. As the IENFa represents the total area of all IENFs, the results also imply global axonal atrophy in diabetic neuropathy.

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Data availability

The data that support the findings of this study contain patient-level clinical and histopathological information and are therefore not publicly available due to privacy and ethical restrictions. Access to the data may be considered upon reasonable request and approval by the corresponding institutional review board. The image analysis codes and scripts used in this study are publicly available in the Supplementary material and at the following URL: https://drive.google.com/file/d/1lPuyZicXxj_jl3CVZLmKtYm6lRVzZ5D8/view?usp=sharing (https://drive.google.com/file/d/1lPuyZicXxj_jl3CVZLmKtYm6lRVzZ5D8/view?usp&;sharing).

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Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 2 funders, 42 references.

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This paper

Hsueh, H.-W., Wu, Y.-Y., Chuang, T.-I., Lin, C.-C., Yeh, T.-Y., Kao, Y.-H., Chang, H.-H., Chao, C.-C., & Hsieh, S.-T. (2026). Machine learning-based skin nerve morphometry for diabetic neuropathy: diagnostic and clinical implications. Brain communications, 8(2), fcag113. https://doi.org/10.1093/braincomms/fcag113

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