A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers

TL;DR

This paper presents a cloud-based deep learning system for automatic detection of diabetic foot ulcers via mobile app, enabling remote diagnosis and monitoring in clinical settings.

Contribution

It introduces a cross-platform mobile framework integrated with a deep CNN deployed on the cloud for real-time ulcer detection from foot images.

Findings

System successfully deployed in clinical settings

Mobile app enables remote patient monitoring

Potential for early detection and improved management

Abstract

This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cross-platform mobile framework which enables the deployment of mobile apps to multiple platforms using a single TypeScript code base. A deep convolutional neural network was deployed to a cloud-based platform where the mobile app could send photographs of patient's feet for inference to detect the presence of diabetic foot ulcers. The functionality and usability of the system were tested in two clinical settings: Salford Royal NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust. The benefits of the system, such as the potential use of the app by patients to identify and monitor their condition are discussed.