A Deep Multi-Modal Method for Patient Wound Healing Assessment

TL;DR

This paper introduces a deep multi-modal approach combining wound images and variables to predict patient hospitalization risk, aiming for early detection and improved wound management.

Contribution

It presents a transfer learning-based wound assessment model that predicts wound variables and healing trajectories from images, advancing wound care diagnostics.

Findings

Effective prediction of wound variables from images

Early detection of wound healing complexities

Potential reduction in clinician diagnosis time

Abstract

Hospitalization of patients is one of the major factors for high wound care costs. Most patients do not acquire a wound which needs immediate hospitalization. However, due to factors such as delay in treatment, patient's non-compliance or existing co-morbid conditions, an injury can deteriorate and ultimately lead to patient hospitalization. In this paper, we propose a deep multi-modal method to predict the patient's risk of hospitalization. Our goal is to predict the risk confidently by collectively using the wound variables and wound images of the patient. Existing works in this domain have mainly focused on healing trajectories based on distinct wound types. We developed a transfer learning-based wound assessment solution, which can predict both wound variables from wound images and their healing trajectories, which is our primary contribution. We argue that the development of a novel model can help in early detection of the complexities in the wound, which might affect the healing process and also reduce the time spent by a clinician to diagnose the wound.