Artificial Intelligence–Based Classification of Diabetic Peripheral Neuropathy From Corneal Confocal Microscopy Images
Open accessAbstract
Diabetic peripheral neuropathy (DPN) is characterized by pain and sensory loss, affecting approximately 50% of patients. Early identification and risk factor management are key to limiting progression of DPN. In contrast to retinopathy (retinal fundus imaging) and nephropathy (microalbuminuria) with early disease detection, the 10-g monofilament identifies advanced DPN. Corneal confocal microscopy (CCM) is an ophthalmic imaging technique that identifies subclinical corneal nerve loss, which predicts incident DPN and has good diagnostic utility for DPN. It also identifies corneal nerve regeneration prior to improvement in symptoms and nerve conduction studies after simultaneous pancreas and kidney transplantation. CCM studies have primarily used manual corneal nerve analysis (CCMetrics), which, although highly reliable, is time-consuming with limited scalability.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous studies explored the development of AI systems to identify diabetic neuropathy based on EHRs or corneal confocal microscopy images, with AUCs ranging from 0.83 to 0.95.
“For neuropathy screening, previous studies explored the development of AI systems to identify diabetic neuropathy based on EHRs116 or corneal confocal microscopy images,117,118,119 with AUCs ranging from 0.83 to 0.95.”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [118]
The paper’s classification results report an AI system using corneal confocal microscopy images to identify diabetic peripheral neuropathy, with AUCs of 0.86 and 0.95 for two comparisons. It also reports an AUC of 1.0 for distinguishing affected patients from controls, so 0.95 does not capture the full range of results in this source. The EHR portion of the citing sentence is attached to a different citation. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSalahouddin et al. reported that a CNN using corneal confocal microscopy images to identify diabetic peripheral neuropathy achieved an AUC of 0.95, sensitivity of 92%, and specificity of 80%.
“the AUC of the system was 0.95, sensitivity was 92%, and specificity was 80%”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Salahouddin et al.118
The paper reports an AUC of 0.95, 92% sensitivity, and 80% specificity specifically for distinguishing patients with diabetes who had DPN from those who did not. These are results for the ANFIS classifier using deep-learning-segmented corneal confocal microscopy images, not for a CNN alone or for DPN identification across all comparison groups. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- [1]Pop-Busui R, Boulton AJ, Feldman EL,. Diabetic neuropathy: a position statement by the American Diabetes Association. Diabetes Care 2017; 40: 136– 154 27999003 PMC6977405
- [2]Pritchard N, Edwards K, Russell AW, Perkins BA, Malik RA, Efron N. Corneal confocal microscopy predicts 4-year incident peripheral neuropathy in type 1 diabetes. Diabetes Care 2015; 38: 671– 675 25573881
- [3]Perkins BA, Lovblom LE, Bril V,. Corneal confocal microscopy for identification of diabetic sensorimotor polyneuropathy: a pooled multinational consortium study. Diabetologia 2018; 61: 1856– 1861 29869146 PMC6061173
- [4]Azmi S, Jeziorska M, Ferdousi M,. Early nerve fibre regeneration in individuals with type 1 diabetes after simultaneous pancreas and kidney transplantation. Diabetologia 2019; 62: 1478– 1487 31175373 PMC6647173
- [5]Petropoulos IN, Alam U, Fadavi H,. Corneal nerve loss detected with corneal confocal microscopy is symmetrical and related to the severity of diabetic polyneuropathy. Diabetes Care 2013; 36: 3646– 3651 23877983 PMC3816900
Cited by
Sorted by citations
Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
- Authors
- Salahouddin, T., Petropoulos, I. N., Ferdousi, M., Ponirakis, G., Asghar, O., Alam, U., Kamran, S., Mahfoud, Z. R., Efron, N., Malik, R. A., & Qidwai, U. A.
- Affiliations
- Qatar University (QA)
- Weill Cornell Medical College in Qatar (QA)
- University of Manchester (GB)
- University of Liverpool (GB)
- Hamad Medical Corporation (QA)
- Queensland University of Technology (AU)
- Corresponding author
- Malik, Rayaz A.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2021-07-01
- Volume
- 44
- Issue
- 7
- Pages
- e151-e153
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC8323170
- OpenAlex
- W3165555128
- Cited by
- 44
- References
- 0
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 15 · 2023: 7 · 2022: 5 · 2021: 3
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- University of Exeter
- Imperial College London
- National Institutes of Health (R105991)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ocular Surface and Contact Lens
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- diabetic peripheral neuropathy, corneal confocal microscopy, artificial intelligence, automated image classification, corneal nerve loss, corneal nerve regeneration, early disease detection, monofilament test
- MeSH terms
- Artificial Intelligence, Cornea, Diabetes Mellitus, Diabetic Neuropathies, Humans, Microscopy, Confocal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
