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基于AI的IOL功率预测公式中的术后数据泄露:一种隐藏的偏差来源

Guillaume Debellemanière¹, Nicole Mechleb¹, Mathieu Gauvin²

¹Department of Ophthalmology, Rothschild Foundation Hospital, Paris, France.

Experimental eye research

|October 29, 2025

概括

预测眼内透镜 (IOL) 功率的人工智能 (AI) 模型可能存在偏差. 在术后结果上训练的模型可能对未来的临床使用不准确,尽管具有良好的回顾性预测.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

使用人工智能来预测眼内透镜 (IOL) 功率的非光学公式通常依赖于术后球形等效 (SE) 作为输入.
在这些AI模型中可能存在关键偏差,影响其临床适用性.

研究的目的:

开发和评估两种使用XGBoost的AI模型来预测IOL功率和术后SE.
评估AI驱动的折射预测公式中的光学逻辑和潜在偏差.

主要方法:

开发了两个XGBoost模型:AI Pred. SE (根据功率预测SE) 和AI预测. 功率 (预测来自SE的功率).
通过Finevision IOLs对4588只眼睛进行训练,并通过独立组进行评估.
通过在一系列值的不同输入 (SE和功率) 来评估模型逻辑.

主要成果:

人工智能预测. 动力模型展示了最低的预测误差,超过了传统公式.
尽管精度很高,但AI Pred. 动力模型显示对目标折射变化的非生理反应.
这表明仅根据回顾性折射结果评估的模型存在偏差.

结论:

使用术后结果评估的纯人工智能公式可能具有基本偏差.
这些模型是为回顾性预测而优化的,但在未来的临床应用中可能会失败.
排除实际的术后折射结果对于开发可靠的AI公式来预测IOL功率至关重要.

关键词:

人工智能的人工智能是人工智能.眼内透镜计算公式眼内透镜计算公式机器学习机器学习

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