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. 2022 Nov 3:10:1053079.
doi: 10.3389/fcell.2022.1053079. eCollection 2022.

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Zhi Fang  1   2 , Zhe Xu  1   2 , Xiaoying He  1   2 , Wei Han  1   2
Affiliations

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program

Zhi Fang et al. Front Cell Dev Biol. .

Abstract

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents' feedback on this system. Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents' evaluations of the AI-based PM identification system were measured by a 17-item questionnaire. Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and "Plus" lesion localization. Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents' performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

Keywords: artificial intelligence; myopic maculopathy; ophthalmology residency training; pathologic myopia; “Plus” lesion.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the study.
FIGURE 2
FIGURE 2
Residents’ feedback to the AI-based PM identification system with a 4-point scale.
See this image and copyright information in PMC

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