开发和评估人工智能模型,以确定眼的目标IOP
Alex Pham1, Edgar Robitaille2, Chris Bradley3
1Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
American journal of ophthalmology
|August 24, 2025
概括
人工智能 (AI) 模型可以预测眼患者的个性化目标眼内压力 (IOP),性能与眼专家相比,在预防视野恶化方面表现优于社会准则.
科学领域:
- 眼科 眼科
- 人工智能
- 医疗诊断
背景情况:
- 眼的治疗依赖于达到目标眼内压力 (IOP),以防止视力丧失.
- 目前设定目标IOP的方法包括临床医生的判断和社会指导方针,有效性各不相同.
- 个性化目标内血压预测可以改善青光眼治疗结果.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于预测青光眼患者的个性化目标眼内压力 (IOP).
- 将AI预测的,临床医生定义的和基于指南的目标内血压对视野恶化的速度的影响进行比较.
主要方法:
- 一个机器学习模型在14871只眼睛的数据集上进行了训练,具有基线结构,功能和临床数据.
- 该模型预测了10559只眼睛的进化数据集的目标内血压,并进行了纵向VF测试.
- 线性模型评估了目标IOP差异对VF恶化的影响 (平均偏差斜率).
主要成果:
- 在预测目标内压时,人工智能模型的平均绝对误差为2.28mmHg.
- AI和临床医生的目标差异对VF结果产生了相似的影响,平均偏差每增加1mmHg分别会加快0. 032dB/ 年和0. 026dB/ 年.
- 无论是AI还是临床医生的目标差异都优于加拿大眼科协会 (COS) 的指导方针,并且在缓解VF恶化方面具有平均绝对IOP.
结论:
- 人工智能模型在设定目标内压力方面表现与眼专家相似.
- 基于人工智能的目标内血压预测优于使用平均内血压或社会指导方针来预防青光眼的进展.
- 需要进一步的研究来评估人工智能引导的目标内血压在更广泛的患者群体中的临床效用.
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