人工智能在越南平定省社区糖尿病视网膜病变筛查中的应用效果。

IF 1 Q4 OPHTHALMOLOGY Taiwan Journal of Ophthalmology Pub Date : 2024-09-13 eCollection Date: 2024-07-01 DOI:10.4103/tjo.TJO-D-23-00101
Thanh Nguyen Van, Hoang Lan Vo Thi
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引用次数: 0

摘要

目的:本研究旨在评估人工智能(AI)在越南平定省社区糖尿病视网膜病变(DR)筛查中的灵敏度、特异性和准确性:这项回顾性、描述性、横断面研究通过分析平定省医院和医疗中心 583 名糖尿病患者的 2332 张非眼动数字眼底照片,评估了 EyeArt 系统 v2.0 的 DR 筛查效果。首先,我们选取了 30 名患者的 120 张数字眼底照片,由两名眼科医生进行 Kappa 指数分析,他们将负责 DR 临床特征评估和 DR 严重程度分级。其次,对所有数字眼底照片进行编码,然后送交上述两位眼科医生,由他们根据国际眼科委员会的指南进行评估和分类。最后,将 EyeArt 的 DR 严重程度量表与眼科医生的量表进行比较,作为 EyeArt 效果的参考标准。所有数据均使用 SPSS 软件 20.0 版进行分析。灵敏度、特异性、阳性预测值、阴性预测值和准确性的数值(置信区间为 95%)根据 DR 状态、可转诊与否和是否威胁视力的 DR 状态进行计算。P<0.05为有统计学意义:EyeArt筛查DR的灵敏度和特异度分别为94.1%和87.2%。可转诊 DR 和视力受威胁 DR 的灵敏度和特异度分别为 96.6%、90.1% 和 100.0%、92.2%。DR筛查、可转诊DR和视力受威胁DR的准确率分别为88.9%、91.4%和93.0%:结论:EyeArt AI 对社区 DR 筛查有效。
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Effectiveness of artificial intelligence for diabetic retinopathy screening in community in Binh Dinh Province, Vietnam.

Purpose: The objective of this study is to evaluate the sensitivity, specificity, and accuracy of artificial intelligence (AI) for diabetic retinopathy (DR) screening in community in Binh Dinh Province in Vietnam.

Materials and methods: This retrospective, descriptive, cross-sectional study assessed the DR screening efficacy of EyeArt system v2.0 by analyzing 2332 nonmydriatic digital fundus pictures of 583 diabetic patients from hospitals and health centers in Binh Dinh province. First, we selected thirty patients with 120 digital fundus pictures to perform the Kappa index by two eye doctors who would be responsible for the DR clinical feature evaluation and DR severity scale classification. Second, all digital fundus pictures were coded and then sent to the two above-mentioned eye doctors for the evaluation and classifications according to the International Committee of Ophthalmology's guidelines. Finally, DR severity scales with EyeArt were compared with those by eye doctors as a reference standard for EyeArt's effectiveness. All the data were analyzed using the SPSS software version 20.0. Values (with confidence interval 95%) of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated according to DR state, referable or not and vision-threatening DR state or not. P < 0.05 was considered statistically significant.

Results: The sensitivity and specificity of EyeArt for DR screening were 94.1% and 87.2%. The sensitivity and specificity for referable DR and vision-threatening DR were 96.6%, 90.1%, and 100.0%, 92.2%. Accuracy for DR screening, referable DR, and vision-threatening DR were 88.9%, 91.4%, and 93.0%, respectively.

Conclusion: EyeArt AI was effective for DR screening in community.

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来源期刊
CiteScore
1.80
自引率
9.10%
发文量
68
审稿时长
19 weeks
期刊最新文献
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