CoChat
Sign Up
Cited by 220Retinal Imaging and AnalysisRetinal Diseases and TreatmentsOphthalmology and Visual Health Research

Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study

Open access
Keel, Stuart, Lee, Pei Ying, Scheetz, Jane, Li, Zhixi, Kotowicz, Mark A., MacIsaac, Richard J., He, Mingguang·2018·Scientific Reports
DOI 10.1038/s41598-018-22612-2PMC PMC5847544
Open this paper in CoChat — free accountOpen PDF

Abstract

The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-018-22612-2

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Only frontline clinicians were interviewed, despite the large number of stakeholders involved in project development and maintenance or impacted directly by the program.

    “Similarly, only frontline clinicians were interviewed despite the large number of stakeholders involved in project development and maintenance (eg, organizational leadership, hospital administrators, data scientists) or impacted directly by the program (ie, patients; [45,46]).”

    From Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study (2020) · cites this paper as [46]

    The Methods (Protocol) and Results (Participant follow-up) say patients were contacted and interviewed about their preferred screening model. The Discussion calls for future research into clinician and organizational stakeholder perspectives, rather than reporting interviews with frontline clinicians. The supplied passages do not give page numbers. (LLM, conf=0.90)

    Checked by a reasoning model — may be wrong.

    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: 20

  1. [1]Foreman, J. et al. The Prevalence and Causes of Vision Loss in Indigenous and Non-Indigenous Australians: The National Eye Health Survey. Ophthalmology, 10.1016/j.ophtha.2017.06.001 (2017). 28689897 · DOI
  2. [2]Magliano DJ Projecting the burden of diabetes in Australia–what is the size of the matter? Australian and New Zealand journal of public health 2009 33 540 543 10.1111/j.1753-6405.2009.00450.x 20078571 · DOI
  3. [4]Tapp RJ The prevalence of and factors associated with diabetic retinopathy in the Australian population Diabetes Care 2003 26 1731 1737 10.2337/diacare.26.6.1731 12766102 · DOI
  4. [5]Cheung N Mitchell P Wong TY Diabetic retinopathy Lancet 2010 376 124 136 10.1016/S0140-6736(09)62124-3 20580421 · DOI · Paper page
  5. [6]Wong TY Cheung CM Larsen M Sharma S Simo R Diabetic retinopathy. Nature reviews Disease primers 2016 2 16012 10.1038/nrdp.2016.12 27159554 · DOI
  6. [8]Nguyen HV Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore Ophthalmology 2016 123 2571 2580 10.1016/j.ophtha.2016.08.021 27726962 · DOI · Paper page
  7. [9]Scanlon PH The English National Screening Programme for diabetic retinopathy 2003-2016 Acta diabetologica 2017 54 515 525 10.1007/s00592-017-0974-1 28224275 PMC5429356 · DOI
  8. [12]Statham MO Sharma A Pane AR Misdiagnosis of acute eye diseases by primary health care providers: incidence and implications Med J Aust 2008 189 402 404 18837685
  9. [13]Oh E Yoo TK Park EC Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study BMC medical informatics and decision making 2013 13 106 10.1186/1472-6947-13-106 24033926 PMC3847617 · DOI
  10. [15]Roychowdhury S Koozekanani DD Parhi KK DREAM: diabetic retinopathy analysis using machine learning IEEE journal of biomedical and health informatics 2014 18 1717 1728 10.1109/JBHI.2013.2294635 25192577 · DOI
  11. [16]Usher D Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening Diabet Med 2004 21 84 90 10.1046/j.1464-5491.2003.01085.x 14706060 · DOI
  12. [17]Gulshan, V. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 10.1001/jama.2016.17216 (2016). 27898976 · DOI · Paper page
  13. [18]Wong TY Bressler NM Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening Jama 2016 316 2366 2367 10.1001/jama.2016.17563 27898977 · DOI
  14. [19]Peto T Tadros C Screening for diabetic retinopathy and diabetic macular edema in the United Kingdom Curr Diab Rep 2012 12 338 345 10.1007/s11892-012-0285-4 22729994 · DOI
  15. [20]Szeged, C., Vanhouck, V., loffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. CVPR (2016).
  16. [21]Crossland L Diabetic Retinopathy Screening and Monitoring of Early Stage Disease in Australian General Practice: Tackling Preventable Blindness within a Chronic Care Model Journal of diabetes research 2016 2016 8405395 10.1155/2016/8405395 26798655 PMC4698989 · DOI
  17. [22]Larizza MF Feasibility of screening for diabetic retinopathy at an Australian pathology collection service: a pilot study Med J Aust 2013 198 97 99 10.5694/mja12.11121 23373500 · DOI
  18. [23]Litchfield I Routine failures in the process for blood testing and the communication of results to patients in primary care in the UK: a qualitative exploration of patient and provider perspectives BMJ quality & safety 2015 24 681 690 10.1136/bmjqs-2014-003690 PMC4680130 26251507 · DOI
  19. [24]Keel S The Prevalence of Diabetic Retinopathy in Australian Adults with Self-Reported Diabetes: The National Eye Health Survey Ophthalmology 2017 124 977 984 10.1016/j.ophtha.2017.02.004 28318640 · DOI · Paper page
  20. [25]Britt H A decade of general practice activity Sydney University Press 2015 39 1 159

Cited by

Sorted by citations

Authors
Keel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J., & He, M.
Affiliations
  • The Royal Victorian Eye & Ear Hospital (AU)
  • Sun Yat-sen University (CN)
  • Barwon Health (AU)
  • The University of Melbourne (AU)
Corresponding author
Keel, Stuart
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2018-03-12
Volume
8
Issue
1
Pages
4330-4330
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-018-22612-2
PMC
PMC5847544
OpenAlex
W2793079232
Cited by
220
References
0
Citations by year
2026: 20 · 2025: 32 · 2024: 26 · 2023: 35 · 2022: 34 · 2021: 35 · 2020: 17 · 2019: 12 · 2018: 9
FWCI
12.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Deakin Research Online (Deakin University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • Bupa Foundation
  • Bupa Health Foundation
  • Barwon Health Foundation
  • St Vincent's Hospital Melbourne
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
  • Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
  • Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
Keywords
diabetic retinopathy, artificial intelligence, deep learning, fundus images, endocrinology outpatient care, diabetes mellitus, patient acceptability, patient satisfaction, screening feasibility, referral accuracy
MeSH terms
Adult, Aged, Aged, 80 and over, Ambulatory Care, Artificial Intelligence, Diabetic Retinopathy, Diagnostic Techniques, Ophthalmological, Feasibility Studies, Female, Humans, Male, Middle Aged, Pilot Projects, Young Adult

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