259-OR: Evaluating Diabetic Foot Ulcer Recognition Algorithms for Patients of Color

by sugaradmin



Introduction and Objective: Computer vision offers an opportunity to automatically analyze foot images to segment diabetic foot ulcers (DFU) and determine clinically relevant characteristics. Although diabetic foot complications take a disproportionate toll on communities of color, these communities are underrepresented in dermatologic image datasets, which may propagate health disparities. Are current state-of-the-art foot ulcer recognition algorithms effective for Black patients living with diabetes (compared to patients with white or pale skin)?Methods: We collected the first known repository of DFU images from patients of color. We train and evaluate five state-of-the-art foot ulcer segmentation models (HarDNet-DFUS, Mask R-CNN, MobileNetV2, U-Net, SegNet) on our dataset, and compare to metrics reported for white or pale skin.Results: Our dataset consists of 3,483 foot images (including 1,652 instances of a DFU or pre-ulcerative lesion) from 248 patients. Images were collected from a diabetes clinic in a safety net hospital in the Southern United States and patients were predominantly (over 80%) Black. The five state-of-the-art ulcer recognition models result in insufficient performance on this new dataset. The best performing baseline model (Mask R-CNN) has been reported to achieve a Dice score (i.e., similarity coefficient indicative of model performance) of 90.2% on a dataset of foot images collected from white patients but achieves a Dice score of only 37.4% on our dataset.Conclusion: Current ulcer recognition models provide lower performance on wound images collected from patients of color. Larger, more diverse datasets will be crucial for the next generation of recognition models. Our work supports more equitable technological interventions for diabetic foot care to improve patient self-monitoring and clinician delivery of care for communities of color. As automated wound recognition may improve clinician delivery of care, it is vital to advance equity for those who face the greatest disease burden.

Disclosure

C. Baseman: None. Z. Leng: None. T. Ploetz: None. G. Santamarina: None. M.C. Schechter: None. M. Fayfman: Research Support; Abbott, Dexcom, Inc. R.I. Arriaga: None.

Funding

American Diabetes Association (11-22-ICTSHD-09)



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