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机器和深度学习方法的多中心评估,以预测绿眼的外科治疗结果

Samuel Barry¹, Sophia Y Wang²

¹Department of Management Science and Engineering, Stanford University, Stanford, CA, United States.

Frontiers in artificial intelligence

|November 7, 2025

概括

机器学习模型可以使用患者的健康记录来预测青光眼手术的成功. 这些工具可以帮助医生识别患有不良结果风险的患者,改善临床决策.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
人工智能在医学中的应用

背景情况:

玻璃眼是全球不可逆转的失明的主要原因.
预测眼外科手术结果对于患者管理和资源分配至关重要.
目前用于预测结果的方法往往缺乏准确性,并依赖于有限的手术前数据.

研究的目的:

开发和评估机器学习 (ML) 和深度学习 (DL) 模型,用于预测眼外科手术结果.
利用手术前电子健康记录 (EHR) 来预测结果,包括眼内压力 (IOP),药物使用和进一步手术的需要.
在一个大型的多中心队列中评估模型性能,并进行广泛的后续跟踪.

主要方法:

分析了10个机构的9,386名接受眼手术的患者队列.
使用手术前的EHR数据来训练ML/DL模型来预测手术失败.
手术失败是由持续升高的内压,增加药物使用或需要重新手术来定义的.
模型性能在内部和外部测试套件上进行了验证.

主要成果:

最好的整体手术失败预测模型是1D卷积神经网络 (1D-CNN),达到76.4%的AUROC.
随机森林,一个经典的ML模型,以76.2%的AUROC实现了可比性能.
模型显示IOP相关失败的预测准确度最高 (82%AUROC),其次是药物使用 (80%) 和需要额外的手术 (68%).
在外部测试装置上,性能略有下降.

结论:

机器学习和深度学习模型在预测眼外科手术结果方面显示出显著的潜力.
手术前的EHR数据是术后成功的有价值预测指标.
这些预测模型可以帮助临床医生识别高风险患者,潜在地改善手术决策和患者护理.

关键词:

临床预测模型的临床预测模型.深度学习是一种深度学习.电子健康记录是电子健康记录.玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼机器学习是机器学习.多中心研究多中心研究.手术结果预测和预测.

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