Development of a localization-based algorithm for the prediction of leg ulcer etiology

Diagnostic work-up of leg ulcers is time- and cost-intensive. This study aimed at evaluating ulcer location as a diagnostic criterium and providing a diagnostic algorithm to facilitate differential diagnosis.

The study consisted of 277 patients with lower leg ulcers. The following five groups were defined: Venous leg ulcer, arterial ulcers, mixed ulcer, arteriolosclerosis, and vasculitis. Using computational surface rendering, predilection sites of different ulcer types were evaluated. The results were integrated in a multinomial logistic regression model to calculate the likelihood of a specific diagnosis depending on location, age, bilateral involvement, and ulcer count. Additionally, neural network image analysis was performed.

The majority of venous ulcers extended to the medial malleolar region. Arterial ulcers were most frequently located on the dorsal aspect of the forefoot. Arteriolosclerotic ulcers were distinctly localized at the middle third of the lower leg. Vasculitic ulcers appeared to be randomly distributed and were markedly smaller, multilocular and bilateral. The multinomial logistic regression model showed an overall satisfactory performance with an estimated accuracy of 0.68 on unseen data.

The presented algorithm based on ulcer location may serve as a basic tool to narrow down potential diagnoses and guide further diagnostic work-up.