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

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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.