Computer-aided diagnostic screening of diabetic peripheral neuropathy using colorimetric membrane analysis

The aim of this study was to develop an objective computational solution, including a smartphone app, to evaluate colorimetric quantification of a membrane designed to diagnose small fiber peripheral neuropathies in individual with diabetes. This membrane, SudoPad, was developed to improve diagnostic speed and accuracy, while minimizing patient discomfort.

SudoPad is polymeric adhesive membrane made from sodium alginate, glycerol, Alizarin Red S, and sodium carbonate. A pilot study for a clinical trial was conducted with three groups to evaluate the membrane's effectiveness. Statistical analysis focused on calculating positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and accuracy.

The SudoPad app demonstrated an PPV of 88.46%, NPV of 85.90%, sensitivity of 67.65% (58,65% to 76,64% - CI 95%), specificity of 95.71% (91,81% to 99,60% - CI 95%) and accuracy of 86.54%. These metrics indicated the system's effectiveness in diagnosing peripheral neuropathies with a significant improvement in diagnostic accuracy.

The results suggest that SudoPad,a low-cost, biodegradable membrane, could enhance health technologies for diagnosing neuropathies. It offers a faster, accurate, and more comfortable alternative to current diagnostic methods.