Development and Evaluation of an Artificial Intelligence Model to Set Target IOP for Glaucoma

IF 4.2 1区医学 Q1 OPHTHALMOLOGY

American Journal of Ophthalmology Pub Date : 2025-08-22 DOI:10.1016/j.ajo.2025.08.046

Alex Pham , Edgar Robitaille , Chris Bradley , Joshua de Souza , Jithin Yohannan

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引用次数: 0

Abstract

Purpose

Develop an artificial intelligence (AI) model to predict a personalized target intraocular pressure (IOP) for eyes with glaucoma. Compare the impact of achieving the AI-predicted, clinician-defined, and society guideline-based target IOP on rates of visual field (VF) worsening.

Design

Development and evaluation of an AI treatment algorithm

Subjects

A dataset of 14,871 eyes with a defined target IOP (set by glaucoma specialists during routine clinical care) and baseline OCT, VF, and clinical measurements. A non-overlapping progression dataset of 10,559 eyes with longitudinal VF testing (≥ 5 reliable tests).

Methods

We trained and tested a machine-learning model on the dataset of eyes with baseline structural, functional, and clinical data to predict a target IOP for each eye in the progression dataset. Linear models estimated the effect of mean target difference (measured IOP – target IOP) on the rate of VF worsening, defined by mean deviation (MD) slope. The effect of deviations from AI targets was compared to deviations from clinician-set targets and targets from society-based guidelines by the Canadian Ophthalmological Society (COS). Linear models also estimated the effect of mean absolute IOP on the rate of VF worsening. The effect of mean target difference (AI, clinician, or COS) was compared to the effect of mean absolute IOP.

Main outcome measures

Effect of 1 mm Hg increase in mean target difference (AI, clinician, or COS) on the rate of MD worsening (dB/year).

Results

The AI model achieved a mean absolute error of 2.28 mm Hg for predicting target IOP. AI and clinician target differences had similar effects on VF outcomes (0.032 dB/year vs 0.026 dB/year faster rate of MD worsening per 1 mm Hg increase, respectively, p = 0.09). AI and clinician target difference had greater effects than COS target difference (0.011 dB/year faster per 1 mm Hg increase, p < 0.001) and mean absolute IOP (0.001 dB/year faster per 1 mm Hg increase, p < 0.001).

Conclusions

AI models can set target IOP with comparable performance to glaucoma specialists and are superior to utilizing mean IOP or society-based guidelines to set targets. Further work is needed to assess the clinical impact of AI-based target IOP guidance for patients managed by non-glaucoma specialists.

查看原文本刊更多论文

青光眼目标眼压人工智能模型的开发与评价

开发人工智能（AI）模型，预测青光眼的个性化目标眼压（IOP）。比较实现人工智能预测、临床定义和基于社会指南的目标IOP对视野恶化率的影响。

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

American Journal of Ophthalmology 医学-眼科学

CiteScore

9.20

自引率

7.10%

发文量

406

审稿时长

36 days

期刊介绍： The American Journal of Ophthalmology is a peer-reviewed, scientific publication that welcomes the submission of original, previously unpublished manuscripts directed to ophthalmologists and visual science specialists describing clinical investigations, clinical observations, and clinically relevant laboratory investigations. Published monthly since 1884, the full text of the American Journal of Ophthalmology and supplementary material are also presented online at www.AJO.com and on ScienceDirect. The American Journal of Ophthalmology publishes Full-Length Articles, Perspectives, Editorials, Correspondences, Books Reports and Announcements. Brief Reports and Case Reports are no longer published. We recommend submitting Brief Reports and Case Reports to our companion publication, the American Journal of Ophthalmology Case Reports. Manuscripts are accepted with the understanding that they have not been and will not be published elsewhere substantially in any format, and that there are no ethical problems with the content or data collection. Authors may be requested to produce the data upon which the manuscript is based and to answer expeditiously any questions about the manuscript or its authors.