Early detection of Diabetic Neuropathy (DN) relies on quantifying Sweat Glands (SGs) function through non-invasive measures of sweat volume and pore activation. Gravimetry, the most common method, measures sweat mass but cannot assess pore activation and is limited by inter- and intra-patient variability. To address this, an optimized starch-iodine test with a stamper-like mechanism was developed to simultaneously quantify sweat volume and pore activation. An algorithm incorporating Structural Similarity Index Measure (SSIM) and its related correction on estimated volume was employed to enhance measurement accuracy. The study aimed to validate this optimized method against the conventional gravimetry test. Volunteers are recruited across four groups (Control, No DN, DN Stage1, DN Stage2). Both tests were conducted on all participants. DN patients showed significantly reduced sweat volume and pore activation compared to healthy individuals and diabetics without neuropathy. The optimized starch-iodine method demonstrated strong agreement with gravimetry (96.3% accuracy) and showed a significant positive correlation (Spearman’s ρ = 0.846, p < 0.001). Microscopic analysis confirmed progressive structural changes in sweat pores among DN patients, supporting the physiological basis for reduced sweat production. Statistical analysis using one-way Analysis of Variance (ANOVA) revealed that forehead sweat volume (F = 59.53, p < 0.001), diabetes duration (F = 182.75, p < 0.001) showed significant differences across groups, indicating their potential utility as distinguishing parameters. Two-way ANOVA confirmed a strong interaction (F = 331.34, p < 0.001) between forehead sweat and starch-iodine outcomes across groups. By capturing nerve damage through SG activity, this approach enables early and accurate DN diagnosis.
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