Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program
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 m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Cell and Developmental Biology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2022.1053079/full |
| _version_ | 1811249474001960960 |
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| author | Zhi Fang Zhi Fang Zhe Xu Zhe Xu Xiaoying He Xiaoying He Wei Han Wei Han |
| author_facet | Zhi Fang Zhi Fang Zhe Xu Zhe Xu Xiaoying He Xiaoying He Wei Han Wei Han |
| author_sort | Zhi Fang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T15:47:07Z |
| format | Article |
| id | doaj.art-0daf7f0bfdd74507b33ece2f1fe1b916 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-04-12T15:47:07Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-0daf7f0bfdd74507b33ece2f1fe1b9162022-12-22T03:26:37ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2022-11-011010.3389/fcell.2022.10530791053079Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training programZhi Fang0Zhi Fang1Zhe Xu2Zhe Xu3Xiaoying He4Xiaoying He5Wei Han6Wei Han7Department of Eye Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaZhejiang Provincial Key Lab of Ophthalmology, Hangzhou, Zhejiang, ChinaDepartment of Eye Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaZhejiang Provincial Key Lab of Ophthalmology, Hangzhou, Zhejiang, ChinaDepartment of Eye Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaZhejiang Provincial Key Lab of Ophthalmology, Hangzhou, Zhejiang, ChinaDepartment of Eye Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaZhejiang Provincial Key Lab of Ophthalmology, Hangzhou, Zhejiang, ChinaBackground: 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.https://www.frontiersin.org/articles/10.3389/fcell.2022.1053079/fullartificial intelligencepathologic myopiamyopic maculopathy“Plus” lesionophthalmology residency training |
| spellingShingle | Zhi Fang Zhi Fang Zhe Xu Zhe Xu Xiaoying He Xiaoying He Wei Han Wei Han Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program Frontiers in Cell and Developmental Biology artificial intelligence pathologic myopia myopic maculopathy “Plus” lesion ophthalmology residency training |
| title | Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program |
| title_full | Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program |
| title_fullStr | Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program |
| title_full_unstemmed | Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program |
| title_short | Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program |
| title_sort | artificial intelligence based pathologic myopia identification system in the ophthalmology residency training program |
| topic | artificial intelligence pathologic myopia myopic maculopathy “Plus” lesion ophthalmology residency training |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2022.1053079/full |
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