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

Area of Science:

• Biomedical Engineering
• Medical Diagnostics
• Polymer Science

Background:

• Small fiber peripheral neuropathies (SFN) are a complication of diabetes.
• Current diagnostic methods for SFN can be slow, inaccurate, and uncomfortable.
• There is a need for objective, rapid, and patient-friendly diagnostic tools.

Purpose of the Study:

• To develop an objective computational solution for diagnosing SFN.
• To create a smartphone application for colorimetric quantification of the SudoPad membrane.
• To improve diagnostic speed, accuracy, and patient comfort for SFN diagnosis.

Main Methods:

• Developed SudoPad, a polymeric adhesive membrane from sodium alginate, glycerol, Alizarin Red S, and sodium carbonate.
• Conducted a pilot clinical trial with three groups to assess membrane effectiveness.
• Utilized statistical analysis including positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and accuracy.

Main Results:

• The SudoPad app achieved a PPV of 88.46% and NPV of 85.90%.
• Demonstrated sensitivity of 67.65% and specificity of 95.71%.
• Overall accuracy of the SudoPad system was 86.54%, indicating significant diagnostic improvement.

Conclusions:

• SudoPad is a low-cost, biodegradable membrane with potential to advance neuropathy diagnostics.
• The SudoPad system offers a faster, more accurate, and comfortable diagnostic alternative.
• This technology enhances health solutions for diagnosing peripheral neuropathies in diabetic patients.