Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation.

Autor: Cassidy B; Department of Computing Mathematics, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK. Electronic address: bill.cassidy@stu.mmu.ac.uk., Hoon Yap M; Department of Computing Mathematics, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK. Electronic address: m.yap@mmu.ac.uk., Pappachan JM; Lancashire Teaching Hospitals NHS Foundation Trust, Preston PR2 9HT, UK. Electronic address: pappachan.joseph@lthtr.nhs.uk., Ahmad N; Manchester University NHS Foundation Trust, Manchester M13 9WL, UK. Electronic address: naseer.ahmad@mft.nhs.uk., Haycocks S; Salford Royal NHS Foundation Trust, Stott Lane, Salford M6 8HD, UK. Electronic address: sam.haycocks@hotmail.co.uk., O'Shea C; Te Whatu Ora Health New Zealand Waikato, Pembroke Street, Hamilton 3240, New Zealand. Electronic address: claire.o'shea@waikatodhb.health.nz., Fernandez CJ; Department of Endocrinology and Metabolism, Pilgrim Hospital, United Lincolnshire Hospitals NHS Trust, Boston LN2 5QY, UK. Electronic address: cornelius.fernandez@ulh.nhs.uk., Chacko E; Jersey General Hospital, The Parade, St Helier, JE1 3QS Jersey, UK. Electronic address: e.chacko@health.gov.je., Jacob K; Eastbourne District General Hospital, Kings Drive, Eastbourne BN21 2UD, UK. Electronic address: k.jacob1@nhs.net., Reeves ND; Faculty of Science & Engineering, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK. Electronic address: n.reeves@mmu.ac.uk.
Jazyk: angličtina
Zdroj: Diabetes research and clinical practice [Diabetes Res Clin Pract] 2023 Nov; Vol. 205, pp. 110951. Date of Electronic Publication: 2023 Oct 15.
DOI: 10.1016/j.diabres.2023.110951
Abstrakt: Objective: Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers.
Methods: The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed.
Results: Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters).
Conclusions: We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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