4646982
doi
10.5281/zenodo.4646982
oai:zenodo.org:4646982
Neil Reeves
Manchester Metropolitan University
Andrew Boulton
University of Manchester and Manchester Royal Infirmary
Satyan Rajbhandari
Lancashire Teaching Hospital
David Armstrong
University of Southern California
Arun G. Maiya
Manipal College and Health Professions and Indian Podiatry Association
Bijan Najafi
Baylor College of Medicine in Texas
Eibe Frank
University of Waikato
Justina Wu
Waikato District Health Board
Diabetic Foot Ulcers Grand Challenge 2021
Moi Hoon Yap
Manchester Metropolitan University
info:eu-repo/semantics/openAccess
Creative Commons Attribution No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nd/4.0/legalcode
MICCAI Challenges
Biomedical Challenges
MICCAI
Diabetic foot ulcers
DFU pathology
Ischemia
Infection
Machine learning
<p>This is the challenge design document for the "Diabetic Foot Ulcers Grand Challenge 2021" Challenge, accepted for MICCAI 2021.</p>
<p>Diabetes is a global epidemic affecting approximately 425 million people. This figure is expected to rise to 629 million people by 2045 [1]. Diabetic Foot Ulcers (DFU) are a serious condition that frequently results from the disease. The rapid rise of the condition over the last few decades is a major challenge for healthcare systems around the world. Cases of DFU frequently lead to more serious conditions, such as infection and ischaemia, that can significantly prolong treatment, and often result in limb amputation, with more serious cases leading to death.</p>
<p>In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focussed on the creation of detection algorithms that could be used as part of a mobile app that patients could use themselves (or a carer/partner) to monitor their condition and to detect the appearance of DFU [2][3]. To this end, the collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospital and the Manchester University NHS Foundation Trust has created an international repository of 6000 DFU images with labelled infection and ischaemia cases for the purpose of supporting research toward more advanced methods of DFU pathology recognition. With joint effort from the lead scientists of the UK, US, India and New Zealand, this challenge will solicit the original works in DFU, promote interactions between researchers and interdisciplinary collaborations.</p>
<p><strong>References</strong></p>
<p>[1] Cho, N., Shaw, J.E., Karuranga, S., Huang, Y., da Rocha Fernandes, J.D. et al. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice, 138(2018): 271-281.<br>
[2] Goyal, M., Reeves, N., Rajbhandari, S., & Yap, M. H. (2019). Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices. IEEE Journal of Biomedical and Health Informatics. 23(4), 1730-1741, doi:10.1109/JBHI.2018.2868656<br>
[3] Yap, M. H., Chatwin, K. E., Ng, C. C., Abbott, C. A., Bowling, F. L., Rajbhandari, S., . . . Reeves, N. D. (2018). A New Mobile Application for Standardizing Diabetic Foot Images. Journal of Diabetes Science and Technology, 12(1), 169-173. doi:10.1177/1932296817713761</p>
Zenodo
2020-03-18
info:eu-repo/semantics/other
3715019
1617106578.024608
2702965
md5:7b97c39ccc29f945e223673bab9dc1d1
https://zenodo.org/records/4646982/files/DiabeticFootUlcersGrandChallenge2021_v2.pdf
public
10.5281/zenodo.3715019
isVersionOf
doi