Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application.
Artificial Intelligence
Diabetic Retinopathy Screening
Machine Learning
Ophthalmologist referral
Sensitivity Specificity study
Journal
Acta diabetologica
ISSN: 1432-5233
Titre abrégé: Acta Diabetol
Pays: Germany
ID NLM: 9200299
Informations de publication
Date de publication:
07 Sep 2023
07 Sep 2023
Historique:
received:
15
05
2023
accepted:
08
08
2023
medline:
7
9
2023
pubmed:
7
9
2023
entrez:
7
9
2023
Statut:
aheadofprint
Résumé
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. This was an observational diagnostic precision study that compared human grader classification with that of DAIRET The rate of cases classified as ungradable was 1.2%, a figure consistent with the literature. DAIRET DAIRET
Identifiants
pubmed: 37676288
doi: 10.1007/s00592-023-02172-2
pii: 10.1007/s00592-023-02172-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. Springer-Verlag Italia S.r.l., part of Springer Nature.
Références
Yau JWY, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35:556–564
doi: 10.2337/dc11-1909
pubmed: 22301125
pmcid: 3322721
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
doi: 10.1016/S2214-109X(17)30393-5
pubmed: 29032195
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
doi: 10.1016/j.ophtha.2011.09.058
Early photocoagulation for diabetic retinopathy (1991) ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology, vol 98, p 766–785
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
doi: 10.1001/archopht.123.3.387
pubmed: 15767483
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)
doi: 10.1016/j.jcjd.2021.01.014
pubmed: 33814308
https://uptodate.com/contents/diabetic-retinopathy-screening Last access 16 Mar 2023
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
https://www.aao.org/education/topic-detail/diabetic-retinopathy-europerly Last access 15 Apr 2023
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
doi: 10.1001/jamanetworkopen.2021.34254
pubmed: 34779843
pmcid: 8593763
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
doi: 10.2337/dc07-1312
pubmed: 18024852
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
doi: 10.2337/dc11-0943
pubmed: 22279031
pmcid: 3322726
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)
doi: 10.1038/s41433-019-0566-0
pubmed: 31488886
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
doi: 10.1016/j.ophtha.2016.11.014
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
doi: 10.1159/000368426
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
doi: 10.1038/s41746-022-00700-y
pubmed: 36307479
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
doi: 10.1136/bjophthalmol-2014-305631
pubmed: 25563767
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)
doi: 10.1016/S2213-8587(19)30411-5
pubmed: 32113513
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
doi: 10.1007/s00592-019-01373-y
pubmed: 31203437