Small fiber neuropathy, including cardiovascular diabetic neuropathy, affects millions of peoples worldwide. The neuropathy is causing disabling burning pain and dysautonomia such as dizziness with standing, brain fog, fatigue, constipation, urinary problems and cold or hot intolerance. Early and accurate diagnosis of neuropathy is essential for correct treatment. Available diagnostic methods are either invasive such as skin biopsy or available only in few specialized centers. This project addresses the limited availability of small fiber neuropathy detection.~The project utilizes utilize specialized image processing of skin pictures for the purpose to extract the statistical features that are related to loss of small fibers. The accuracy of the diagnosis verified using skin biopsies.~This approach can improve the availability of diagnosis of small fiber neuropathy.
Small fiber neuropathy affects millions of peoples worldwide. The neuropathy is causing disabling burning pain and dysautonomia such as dizziness with standing, brain fog, fatigue, constipation, too much or too little sweating. The detection of nerve damage is complicated and not widely available; it requires either skin biopsy or specialized equipment and training. This project utilizes the mathematical processing of skin pictures for the purpose to extract the statistical features related to loss of small fibers. This approach can improve the availability of diagnosis of small fiber neuropathy.