Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis.

IF 2 4区 医学 Q2 OPHTHALMOLOGY Journal of Glaucoma Pub Date : 2024-09-01 Epub Date: 2024-05-16 DOI:10.1097/IJG.0000000000002428
Marco A C Olyntho, Carlos A C Jorge, Everton B Castanha, Andreia N Gonçalves, Barbara L Silva, Bernardo V Nogueira, Geovana M Lima, Carolina P B Gracitelli, Andrew J Tatham
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Abstract

Prcis: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively.

Purpose: This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma.

Methods: We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity.

Results: The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI: 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI: 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1%-95.1%. Similarly, specificity results ranged from 90.3% to 94.5% with the removal of each individual study and recalculation of pooled specificity.

Conclusion: DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.

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人工智能在前房评估中的应用:系统回顾与元分析》。
目的:本研究旨在回顾文献,比较应用于眼前节光学相干断层扫描图像(AS-OCT)的深度学习算法(DLA)与眼底镜检查在检测青光眼患者闭角方面的准确性:我们进行了一项系统综述和荟萃分析,评估了在 AS-OCT 图像中使用 DLA 与眼底镜检查相比在诊断闭角方面的效果。我们检索了 PubMed、Scopus、Embase、Lilacs、Scielo 和 Cochrane Central Register of Controlled Trials。结果:初步检索发现了 214 项研究,其中 6 项被纳入最终分析。研究总人数包括 5,269 名患者。与眼底镜检查相比,DLA的综合灵敏度为94.0%(95% CI 83.8%-97.9%),而集合特异性为93.6%(95% CI 85.7%-97.3%)。除去每项研究的灵敏度分析表明,汇总灵敏度在 90.1% 到 95.1% 之间。同样,剔除每项研究并重新计算集合特异性后,特异性结果在90.3%至94.5%之间:结论:DLA 应用于 AS-OCT 在识别闭角方面具有出色的灵敏度和特异性。这项技术对于无法获得经验丰富的眼科医生进行眼底检查的人群来说,可能是一项宝贵的筛查资源。
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来源期刊
Journal of Glaucoma
Journal of Glaucoma 医学-眼科学
CiteScore
4.20
自引率
10.00%
发文量
330
审稿时长
4-8 weeks
期刊介绍: The Journal of Glaucoma is a peer reviewed journal addressing the spectrum of issues affecting definition, diagnosis, and management of glaucoma and providing a forum for lively and stimulating discussion of clinical, scientific, and socioeconomic factors affecting care of glaucoma patients.
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