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. 2023 Apr 24;22(1):38.
doi: 10.1186/s12938-023-01097-9.

Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening

Shujuan Cao #  1 , Rongpei Zhang #  2   1 , Aixin Jiang  1 , Mayila Kuerban  1 , Aizezi Wumaier  1 , Jianhua Wu  1 , Kaihua Xie  1 , Mireayi Aizezi  1 , Abudurexiti Tuersun  1 , Xuanwei Liang  3   4 , Rongxin Chen  5   6
Affiliations

Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening

Shujuan Cao et al. Biomed Eng Online. .

Abstract

Background: To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment.

Methods: A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening.

Results: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening.

Conclusions: In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.

Keywords: Artificial intelligence; Color fundus photography; Early screening; Ocular fundus abnormalities; Prevention of blindness.

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Conflict of interest statement

None of the authors have any financial/conflicting interests to disclose.

Figures

Fig. 1
Fig. 1
Percentage of diagnostic results for AI and gold standard referral. The percentages of 7 conditions in clinical application environment and population screening are shown and compared. ARMD age-related macular degeneration, DR diabetic retinopathy, RVO retinal vein occlusion, PM pathological myopia
Fig. 2
Fig. 2
Comparison of age and sex in the clinical AI application environment and population screening. a Comparison of the average age in the clinical environment and population screening. b Comparison of the sex percentage in the clinical environment and population screening
Fig. 3
Fig. 3
Comparison of different diagnostic results in AI clinical examination. a Sensitivity comparison of 7 diagnostic results. b Specificity comparison of 7 diagnostic results. c Accuracy comparison of 7 diagnostic results. d PPV comparison of 7 diagnostic results. e NPV comparison of 7 diagnostic results. PPV, positive predictive value; NPV, negative predictive value
Fig. 4
Fig. 4
The receiver operating characteristic curve for every condition. a ROC curve and AUC for normal. b ROC curve and AUC for ARMD. c ROC curve and AUC for DR. d ROC curve and AUC for RVO. e ROC curve and AUC for referable glaucoma. f ROC curve and AUC for PM. g ROC curve and AUC for other abnormalities. AUC, areas under receiver operating characteristic curve
Fig. 5
Fig. 5
Flowchart for testing the AI fundus screening system
See this image and copyright information in PMC

References

    1. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. doi: 10.1038/s41746-018-0040-6. - DOI - PMC - PubMed
    1. Grassmann F, Mengelkamp J, Brandl C, et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. 2018;125(9):1410–1420. doi: 10.1016/j.ophtha.2018.02.037. - DOI - PubMed
    1. Karagiannis D, Kontadakis GA, Klados NE, et al. Central retinal vein occlusion and pseudoexfoliation syndrome. Clin Interv Aging. 2015;10:879–883. - PMC - PubMed
    1. Nagasato D, Tabuchi H, Masumoto H, et al. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PLoS ONE. 2019;14(11):e0223965. doi: 10.1371/journal.pone.0223965. - DOI - PMC - PubMed
    1. Li Y, Feng W, Zhao X, et al. Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images. Br J Ophthalmol. 2022;106(5):633–639. doi: 10.1136/bjophthalmol-2020-317825. - DOI - PMC - PubMed