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Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application

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Abstract

Aims

Periodical screening for diabetic retinopathy (DR) by an ophthalmologist is expensive and demanding. Automated DR image evaluation with Artificial Intelligence tools may represent a clinical and cost-effective alternative for the detection of retinopathy. We aimed to evaluate the accuracy and reliability of a machine learning algorithm.

Methods

This was an observational diagnostic precision study that compared human grader classification with that of DAIRET®, an algorithm nested in an electronic medical record powered by Retmarker SA. Retinal images were taken from 637 consecutive patients attending a routine annual diabetic visit between June 2021 and February 2023. They were manually graded by an ophthalmologist following the International Clinical Diabetic Retinopathy Severity Scale and the results were compared with those of the AI responses. The main outcome measures were screening performance, such as sensitivity and specificity and diagnostic accuracy by 95% confidence intervals.

Results

The rate of cases classified as ungradable was 1.2%, a figure consistent with the literature. DAIRET® sensitivity in the detection of cases of referable DR (moderate and above, “sight-threatening” forms of retinopathy) was equal to 1 (100%). The specificity, that is the true negative rate of absence of DR, was 80 ± 0.04.

Conclusions

DAIRET® achieved excellent sensitivity for referable retinopathy compared with that of human graders. This is undoubtedly the key finding of the study and translates into the certainty that no patient in need of the ophthalmologist is misdiagnosed as negative. It also had sufficient specificity to represent a cost-effective alternative to manual grade alone.

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References

  1. Yau JWY, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35:556–564

    Article  PubMed  PubMed Central  Google Scholar 

  2. Flaxman SR, Bourne RRA, Resnikoff S et al (2017) Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health 5:e1221–e1234

    Article  PubMed  Google Scholar 

  3. Aiello LP, Beck RW et al (2011) Rationale for the diabetic retinopathy clinical research network treatment protocol for center-involved diabetic macular edema. Ophthalmology 118:5–14

    Article  Google Scholar 

  4. Early photocoagulation for diabetic retinopathy (1991) ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology, vol 98, p 766–785

  5. Hartnett ME, Key IJ, Loyacano NM et al (2005) Perceived barriers to diabetic eye care: qualitative study of patients and physicians. Arch Ophthalmol 123:387–391

    Article  PubMed  Google Scholar 

  6. Egunsola O, Dowsett LE, Diaz R et al (2021) Diabetic retinopathy screening: a systematic review of qualitative literature. Can J Diabetes 45(8):725-733.e12. https://doi.org/10.1016/j.jcjd.2021.01.014. (Epub 2021 Feb 3)

    Article  PubMed  Google Scholar 

  7. https://uptodate.com/contents/diabetic-retinopathy-screening Last access 16 Mar 2023

  8. Piatti A, Doglio M, Tartaglino B et al (2022) Diabetic retinopathy screening with artificial intelligence: a pivotal experience in italian healthcare system—preliminary report. Diabetes Obes Int J 7(S1):0001S1-008

    Google Scholar 

  9. https://www.aao.org/education/topic-detail/diabetic-retinopathy-europerly Last access 15 Apr 2023

  10. Ipp E, Liljenquist D, Bode B et al (2021) Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy. JAMA Netw Open 4(11):e2134254

    Article  PubMed  PubMed Central  Google Scholar 

  11. Abramoff MD, Niemeijer M, Suttorp-Schulten MSA et al (2008) Evaluation of a system for automatic detection of diabetic retinopathy from colour fundus photographs in a large population of patients with diabetes. Diabetes Care 31:193–198

    Article  PubMed  Google Scholar 

  12. Jones CD, Greenwood RH, Misra A et al (2012) Incidence and progression of diabetic retinopathy during 17 years of a population-based screening program in England. Diabetes Care 35:592–596

    Article  PubMed  PubMed Central  Google Scholar 

  13. Grzybowski A, Brona P, Lim G et al (2020) Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond) 34(3):451–460. https://doi.org/10.1038/s41433-019-0566-0. (Epub 2019 Sep 5)

    Article  PubMed  Google Scholar 

  14. Tufail A, Rudisill C, Egan C et al (2017) Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophtalmology 124(3):343–351

    Article  Google Scholar 

  15. Ribeiro L, Oliveira CM, Neves C et al (2015) Screening for diabetic retinopathy in the central region of Portugal. Added value of automated ‘disease/no disease’ grading. Ophthalmologica 233:96–103

    Article  Google Scholar 

  16. Ciecierski-Holmes T, Singh R, Axt M, Brenner S et al (2022) Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. Review NPJ Digit Med 5(1):162. https://doi.org/10.1038/s41746-022-00700-y

    Article  PubMed  Google Scholar 

  17. Shi L, Wu H, Dong J et al (2015) Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis. Br J Ophthalmol 99(6):823

    Article  PubMed  Google Scholar 

  18. Vujosevic S, Aldington SJ, Silva P et al (2020) Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol 8(4):337–347. https://doi.org/10.1016/S2213-8587(19)30411-5. (Epub 2020 Feb 27)

    Article  PubMed  Google Scholar 

  19. Cicinelli MV, Cavalleri M, Brambati M, Lattanzio R, Bandello F (2019) New imaging systems in diabetic retinopathy. Acta Diabetol 56(9):981–994. https://doi.org/10.1007/s00592-019-01373-y

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Eye Unit, Primary Care, ASL TO5, Regione Piemonte, 10024, Moncalieri, TO, Italy

    A. Piatti

  2. Metabolism and Diabetes Unit, ASL TO5, Regione Piemonte, Chieri (TO), Italy

    F. Romeo, R. Manti, M. Doglio, E. Nada & C. B. Giorda

  3. Chaira Medica Association, Chieri (TO), Italy

    B. Tartaglino

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  1. A. Piatti
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  2. F. Romeo
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  3. R. Manti
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  4. M. Doglio
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  5. B. Tartaglino
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  6. E. Nada
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  7. C. B. Giorda
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Contributions

PA: Conceptualization, Methodology, Writing—Original draft preparation and Supervision. RF: Data curation, Writing—Reviewing and Editing. MR: Data curation, Writing—Reviewing and Editing. TB: Data curation, Writing—Reviewing and Editing. NE: Data curation, Writing—Reviewing and Editing. GCB: Conceptualization, Methodology, Writing—Original draft preparation and Supervision.

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Correspondence to A. Piatti.

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The authors declare that they have no conflicts of interest.

Ethical standard statement

The study was approved by the Local Ethics Committee.

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This article belongs to the topical collection Eye Complications of Diabetes, managed by Giuseppe Querques.

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Piatti, A., Romeo, F., Manti, R. et al. Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application. Acta Diabetol 61, 63–68 (2024). https://doi.org/10.1007/s00592-023-02172-2

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  • DOI: https://doi.org/10.1007/s00592-023-02172-2

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