CATALYZE: a deep learning approach for cataract assessment and grading on SS-OCT images.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Journal of cataract and refractive surgery Pub Date : 2025-03-01 DOI:10.1097/j.jcrs.0000000000001598

Christophe Panthier, Pierre Zeboulon, Helene Rouger, Jacques Bijon, Damien Gatinel

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引用次数: 0

Abstract

Purpose: To assess a new objective deep learning model cataract grading method based on swept-source optical coherence tomography (SS-OCT) scans provided by the Anterion.

Setting: Single-center study at the Rothschild Foundation, Paris, France.

Design: Prospective cross-sectional study.

Methods: All patients consulting for cataract evaluation and consenting to study participation were included. History of previous ocular surgery, cornea or retina disorders, and ocular dryness were exclusion criteria. Our CATALYZE pipeline was applied to Anterion image providing layerwise cataract metrics and an overall clinical significance index (CSI) of cataract. Ocular scatter index (OSI) was also measured with a double-pass aberrometer (OQAS) and compared with our CSI.

Results: 548 eyes were included, 331 in the development set (48 with cataract and 283 controls) and 217 in the validation set (85 with cataract and 132 controls) of 315 patients aged 19 to 85 years (mean ± SD: 50 ± 21 years). The CSI correlated with the OSI ( r2 = 0.87, P < .01). CSI area under the receiver operating characteristic curve (AUROC) was comparable with OSI AUROC (0.985 vs 0.981 respectively, P > .05) with 95% sensitivity and 95% specificity.

Conclusions: The deep learning pipeline CATALYZE based on Anterion SS-OCT may be a reliable and comprehensive objective cataract grading method.

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催化：一种深度学习方法，用于对ss-oct正面图像进行白内障评估和分级。

目的：评估由Anterion®（Heidelberg, Germany）提供的基于扫源光学相干断层扫描（SS-OCT）扫描的一种新的客观深度学习模型白内障分级方法。环境：法国巴黎罗斯柴尔德基金会的单中心研究。设计:前瞻性横断面研究。方法：纳入所有咨询白内障评估并同意参与研究的患者。排除标准为既往眼部手术史、角膜或视网膜疾病、眼干涩。我们的catalyst流水线应用于Anterion®图像，提供分层白内障指标和白内障的总体临床意义指数（CSI）。眼散射指数（OSI）也用双通像差仪（OQAS®）测量，并与我们的CSI进行比较。结果：共纳入548只眼，其中发育组331只眼（48只白内障，283只对照），验证组217只眼（85只白内障，132只对照），年龄为315例（平均±SD: 50±21岁）。CSI与OSI相关（r2 = 0.87, p0.05），敏感性为95%，特异性为95%。结论：我们基于Anterion®SS-OCT的深度学习管道catalyst是一种可靠、全面、客观的白内障分级方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

$Journal of cataract and refractive surgery$

Journal of cataract and refractive surgery 医学-外科

CiteScore

5.60

自引率

14.30%

发文量

259

审稿时长

8.5 weeks

期刊介绍： The Journal of Cataract & Refractive Surgery (JCRS), a preeminent peer-reviewed monthly ophthalmology publication, is the official journal of the American Society of Cataract and Refractive Surgery (ASCRS) and the European Society of Cataract and Refractive Surgeons (ESCRS). JCRS publishes high quality articles on all aspects of anterior segment surgery. In addition to original clinical studies, the journal features a consultation section, practical techniques, important cases, and reviews as well as basic science articles.