HealTech - A System for Predicting Patient Hospitalization Risk and Wound Progression in Old Patients

Subba Reddy Oota, Vijay Rowtula, Shahid Mohammed, Jeffrey Galitz, Minghsun Liu, Manish Gupta; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2463-2472

How bad is my wound? How fast will the wound heal? Do I need to get hospitalized? Questions like these are critical for wound assessment, but challenging to answer. Given a wound image and patient attributes, our goal is to build models for two wound assessment tasks: (1) predicting if the patient needs hospitalization for the wound to heal, and (2) estimating wound progression, i.e., weeks to heal. The problem is challenging because wound progression and hospitalization risk depend on multiple factors that need to be inferred automatically from the given wound image. There exists no work which performs a rigorous study of wound assessment tasks considering multiple wound attributes inferred using a large dataset of wound images. We present HealTech, a two-stage wound assessment solution. The first stage predicts various wound attributes (like ulcer type, location, stage, etc.) from wound images, using deep neural networks. The second stage predicts (1) whether the wound would heal (using conventional in-house treatment) or not (needs hospitalization), and (2) the number of weeks to heal, using an evolutionary algorithm based stacked Light Gradient Boosted Machines (LGBM) model. On a large dataset of 125711 wound images, HealTech achieves a recall of 83 and a precision of 92 for wounds with the risk of hospitalization. For wounds that can be healed without hospitalization, precision and recall are as high as 99. Our wound progression model provides a mean absolute error of 3.3 weeks.