March 18, 2020 Other Open Access

Moi Hoon Yap; Neil Reeves; Andrew Boulton; Satyan Rajbhandari; David Armstrong; Arun G. Maiya; Bijan Najafi; Eibe Frank; Justina Wu

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  <identifier identifierType="DOI">10.5281/zenodo.3731068</identifier>
  <creators>
    <creator>
      <creatorName>Moi Hoon Yap</creatorName>
      <affiliation>Manchester Metropolitan University</affiliation>
    </creator>
    <creator>
      <creatorName>Neil Reeves</creatorName>
      <affiliation>Manchester Metropolitan University</affiliation>
    </creator>
    <creator>
      <creatorName>Andrew Boulton</creatorName>
      <affiliation>University of Manchester and Manchester Royal Infirmary</affiliation>
    </creator>
    <creator>
      <creatorName>Satyan Rajbhandari</creatorName>
      <affiliation>Lancashire Teaching Hospital</affiliation>
    </creator>
    <creator>
      <creatorName>David Armstrong</creatorName>
      <affiliation>University of Southern California</affiliation>
    </creator>
    <creator>
      <creatorName>Arun G. Maiya</creatorName>
      <affiliation>Manipal College and Health Professions and Indian Podiatry Association</affiliation>
    </creator>
    <creator>
      <creatorName>Bijan Najafi</creatorName>
      <affiliation>Baylor College of Medicine in Texas</affiliation>
    </creator>
    <creator>
      <creatorName>Eibe Frank</creatorName>
      <affiliation>University of Waikato</affiliation>
    </creator>
    <creator>
      <creatorName>Justina Wu</creatorName>
      <affiliation>Waikato District Health Board</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Diabetic Foot Ulcers Grand Challenge 2020</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>MICCAI Challenges</subject>
    <subject>Biomedical Challenges</subject>
    <subject>MICCAI</subject>
    <subject>Diabetic foot ulcer</subject>
    <subject>DFU detection</subject>
    <subject>Ulcer monitoring</subject>
    <subject>Machine learning</subject>
    <subject>Digital health</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-03-18</date>
  </dates>
  <resourceType resourceTypeGeneral="Other"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3731068</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3715015</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nd/4.0/legalcode">Creative Commons Attribution No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This is the challenge design document for the &amp;quot;Diabetic Foot Ulcers Grand Challenge 2020&amp;quot; Challenge, accepted for MICCAI 2020.&lt;/p&gt;

&lt;p&gt;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. 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 a repository of 4500 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. With joint effort from the lead scientists of the UK, US, India and New Zealand, this challenge will solicit the original works in DFU, and promote interactions between researchers and interdisciplinary collaborations.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;[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.&lt;br&gt;
[2] Goyal, M., Reeves, N., Rajbhandari, S., &amp;amp; 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&lt;br&gt;
[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&lt;/p&gt;</description>
  </descriptions>
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