Fully Automated Diabetic Foot Ulcer Detection

Cross-Platform Mobile App

FootSnap AI uses a mobile app to send photos of patient's feet to our cloud-based AI technology. The app runs on Android and iOS devices.

Cloud-Based AI Technology

FootSnap AI utilises cutting-edge deep learning technologies to accuractely detect ulcers on photos of patient's feet.

Easy to Use

The app has been designed primarily with usability in mind, so that patients, their family members, and carers, can use it with no specialist knowledge.

Fully Automated Detection

FootSnap AI is a novel healthcare technology developed to support the global burden of diabetic foot problems. FootSnap AI is the result of years of scientific research by a group of academics, medical professionals and experts in cloud technology. Pioneered by Prof. Neil Reeves and Dr. Moi Hoon Yap from the Manchester Metropolitan University, this technology is currently transitioning to real-world application. Our key software developer, Bill Cassidy, has successfully scaled-up FootSnap AI on Oracle Cloud Infrastructure.

Footsnap AI Overview

Click the video below for details outlining the scope of the project.



Click here to watch Dr Moi Hoon Yap (co-inventor) and Dr Naseer Ahmad (Vascular Surgeon) discuss FootSnap AI.


Latest Developments

- Ongoing proof-of-concept work at Salford Royal Hospital and Lancashire Teaching Hospitals

- MICCAI Diabetic Foot Ulcer Grand Challenge: https://dfu-challenge.github.io

Scientific Publications

FootSnap AI is supported by scientific evidence. References and links to all relevant scientific papers are provided below (click links to expand sections and view details):

B. Cassidy, N. D. Reeves, J. M. Pappachan, N. Ahmad, S. Haycocks, D. Gillespie, M. Yap, A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers, IEEE Pervasive Computing, no. 1, 2022. To download: https://doi.org/10.1109/MPRV.2021.3135686
M. Goyal, N. D. Reeves, S. Rajbhandari, N. Ahmad, C. Wang, M. H. Yap, Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques, Computers in Biology and Medicine, vol. 117, 2020. To download: https://doi.org/10.1016/j.compbiomed.2020.103616
M. Goyal, N. D. Reeves, S. Rajbhandari, M. H. Yap, Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices, IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1730-1741, 2019. To download: https://ieeexplore.ieee.org/document/8456504
M. H. Yap, K. E. Chatwin, C. C. Ng, C. A. Abbott, F. L. Bowling, S. Rajbhandari, A. Boulton, N. D. Reeves, A New Mobile Application for Standardizing Diabetic Foot Images. Journal of Diabetes Science and Technology, vol. 12, no. 1, pp. 169–173, 2018. To download: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761973/
M. Goyal, M. H. Yap, N. D. Reeves, S. Rajbhandari, J.Spragg, Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation, IEEE International Conference on Systems, Man, and Cybernetics (SMC) Banff, AB, pp. 618-623, 2017. To download: https://ieeexplore.ieee.org/document/8122675
M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, M. H. Yap, DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification, IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-12, 2018. To download: https://ieeexplore.ieee.org/document/8464076
M. H. Yap, C. C. Ng, K. Chatwin, C. A. Abbott, F. L. Bowling, A. J. Boulton, N. D. Reeves, Computer Vision Algorithms in the Detection of Diabetic Foot Ulceration: A New Paradigm for Diabetic Foot Care? Journal of Diabetes Science and Technology, vol. 10, no. 2, pp. 612–613, 2016. To download: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4773968/

Media Coverage

Forbes

20th Mar 2020

Diabetes Times

5th September 2017

HealthTech Insider

8th September 2017

Our Partners

Oracle Logo
NHS Salford Royal Foundation Trust Logo
NHS Lancashire Teaching Hospitals Foundation Trust Logo
NHS Manchester Foundation Trust Logo