Multi-Analyte, Swab-based Automated Wound Monitor with AI

TL;DR

This paper presents a low-cost, multi-analyte swab-based wound sensor integrated with an iOS app that uses computer vision to automatically assess wound severity and monitor healing in real-time.

Contribution

It introduces a novel integrated system combining 3D printed assays, automated image analysis, and a mobile app for early detection and monitoring of non-healing diabetic foot ulcers.

Findings

Automated computer vision techniques accurately assess wound severity.

The integrated system enables real-time wound monitoring and data analysis.

The approach is robust against variations in camera and ambient conditions.

Abstract

Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that develop to chronic wounds early can drastically reduce treatment costs and minimize risks of amputation. There is therefore a pressing need for diagnostic tools that can detect non-healing DFUs early. We develop a low cost, multi-analyte 3D printed assays seamlessly integrated on swabs that can identify non-healing DFUs and a Wound Sensor iOS App - an innovative mobile application developed for the controlled acquisition and automated analysis of wound sensor data. By comparing both the original base image (before exposure to the wound) and the wound-exposed image, we developed automated computer vision techniques to compare density changes between the two assay images, which allow us to automatically determine the severity of the wound. The iOS app ensures accurate data collection and presents actionable insights, despite challenges such as variations in camera configurations and ambient conditions. The proposed integrated sensor and iOS app will allow healthcare professionals to monitor wound conditions real-time, track healing progress, and assess critical parameters related to wound care.