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Multicenter Study
. 2025 Apr 8;25(1):503.
doi: 10.1186/s12909-025-07066-1.

The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study

Meng Wang  1   2 , Xiao Zhang  1   2 , Donghui Li  1   2 , Qijie Wei  3 , Jianchun Zhao  4 , Xiang Gao  5 , Tianhui Shan  5 , Hao Feng  6 , Guolong Ding  7 , Chan Li  7 , Binghui Wu  7 , Xirong Li  3 , Chan Wu  8   9 , Weihong Yu  10   11
Affiliations
Multicenter Study

The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study

Meng Wang et al. BMC Med Educ. .

Abstract

Objective: To assess the potential of artificial intelligence reading label system on the training of ophthalmologists in a multicenter bimodal multi-disease study.

Methods: The accuracy of 16 ophthalmologists with study duration ranging from one to nine years across multiple annotation rounds and its correlation with the number of rounds and ophthalmology study duration were analyzed. Additionally, this study evaluated the concordance between optical coherence tomography (OCT) or color fundus photography (CFP) and final case diagnosis.

Results: The study involved 7777 pairs of OCT and CFP images, cases labeled with nine prevalent retinal diseases including diabetic retinopathy (DR, 2118 cases), retinal detachment (RD, 121 cases), retinal vein occlusion (RVO, 886 cases), dry age-related macular degeneration (dAMD, 549 cases), wet age-related macular degeneration (wAMD, 1023 cases), epiretinal membrane (ERM, 1061 cases), central serous retinopathy (CSC, 150 cases), macular schisis (MS, 128 cases), macular hole (MH, 86 cases) and normal fundus (1036 cases) were selected for further analysis. All images were assigned to 16 ophthalmologists over five rounds. The average diagnostic accuracy for the nine retinal diseases and normal fundus improved significantly across the five rounds (p = 0.013) and is closely correlated to the duration of ophthalmology study (p = 0.007). Furthermore, significant improvements were observed in the diagnostic accuracy of both OCT (p = 0.028) and CFP (p = 0.021) modalities as the number of rounds increased. Notably, OCT single modal diagnosis demonstrated higher consistency with the final diagnosis in cases of RD, ERM, MS, and MH compared to CFP, while CFP single modal diagnosis has higher consistency in DR, RVO and normal fundus.

Conclusion: The implementation of an artificial intelligence reading label system enhances the diagnostic accuracy of retinal diseases among ophthalmologists and holds potential for integration into future medical education.

Keywords: Artificial intelligence; Diagnostic accuracy; Medical education; Reading label system; Retinal diseases.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Our study agreed with the tenets of the Declaration of Helsinki. Ethics approval was approved from the PUMC Hospital Institutional Review Board (K3606). The PUMC Hospital Institutional Review Board has approved the waiver of informed consent for participants in this study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The increase of average accuracy in bimodal diagnosis, OCT diagnosis and CFP diagnosis. The average accuracy increased with the number of rounds increasing (A), in bimodal diagnosis (B), OCT single modal diagnosis (C), and CFP single modal diagnosis (D)
Fig. 2
Fig. 2
Typical images with different labels in OCT and CFP. A1 and A2: CFP labeled with wAMD, OCT labeled wit wAMD and RD; B1 and B2: CFP labeled with ERM, OCT labeled with ERM and MH; C1 and C2: CFP labeled with BRVO, OCT labeled with normal fundus; D1 and D2: CFP labeled with DR, OCT labeled with normal fundus
See this image and copyright information in PMC

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References

    1. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. - PubMed
    1. Li JO, Liu H, Ting DSJ, Jeon S, Chan RVP, Kim JE, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res. 2021;82:100900. - PMC - PubMed
    1. Hasei J, Nakahara R, Otsuka Y, Nakamura Y, Hironari T, Kahara N, et al. High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis. Cancer Sci. 2024;115(11):3695–704. - PMC - PubMed
    1. Wang S, He X, Jian Z, Li J, Xu C, Chen Y, et al. Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review. Eye Vis (Lond). 2024;11(1):38. - PMC - PubMed
    1. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46.

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