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Cited by 70Ocular Surface and Contact LensArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

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Preston, Frank G., Meng, Yanda, Burgess, Jamie, Ferdousi, Maryam, Azmi, Shazli, Petropoulos, Ioannis N., Kaye, Stephen, Malik, Rayaz A. +2 more·2022·Diabetologia
DOI 10.1007/s00125-021-05617-xPMC PMC8803718
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Abstract

Aims/hypothesis: We aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of). Methods: The AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm’s generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN−) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN−, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm. Results: The results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F1-score of 0.91 (95% CI 0.79, 1.0); PN−: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F1-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F1-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN− and an absence of corneal nerves for PN+ images. Conclusions/interpretation: We demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy. Graphical abstract:

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Cited by

Sorted by citations

Authors
Preston, F. G., Meng, Y., Burgess, J., Ferdousi, M., Azmi, S., Petropoulos, I. N., Kaye, S., Malik, R. A., Zheng, Y., & Alam, U.
Affiliations
  • University of Liverpool (GB)
  • University of Manchester (GB)
  • Weill Cornell Medical College in Qatar (QA)
Corresponding author
  • Zheng, Yalin
  • Alam, Uazman
ORCID
Venue
Diabetologia
Publisher
Springer Science and Business Media LLC
Published
2021-11-21
Volume
65
Issue
3
Pages
457-466
ISSN
  • 0012-186X (ISSN-L)
  • 1432-0428
DOI
10.1007/s00125-021-05617-x
PMC
PMC8803718
OpenAlex
W3215647506
Cited by
70
References
6
Citations by year
2026: 10 · 2025: 16 · 2024: 16 · 2023: 20 · 2022: 8
FWCI
9.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Diabetologia (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Juvenile Diabetes Research Foundation (8-2008-362)
  • National Institutes of Health (1DP3DK104386-01)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ocular Surface and Contact Lens
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
diabetic peripheral neuropathy, corneal confocal microscopy, artificial intelligence, deep learning, corneal nerves, prediabetes, type 1 diabetes, type 2 diabetes, Grad-CAM, occlusion sensitivity, ResNet, neuropathy diagnosis, diabetic neuropathy screening
MeSH terms
Artificial Intelligence, Diabetes Mellitus, Type 2, Diabetic Neuropathies, Humans, Prediabetic State, Microscopy, Confocal

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