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优化预测模型远距离和近距离视觉结果在近视眼手术中使用机器学习

Soodabeh Darzi¹, Pierre Baudu², Franck Penin³

¹SCHWIND eye-tech-solutions, Kleinostheim, Germany.

Translational vision science & technology

|October 24, 2025

概括

机器学习模型预测了长视眼角膜折射手术后的视觉结果. 距离校正视觉增益 (DCVG) 度量有助于平衡近距离视觉和远距离视觉,以实现个性化的手术规划.

科学领域:

眼科医生眼科眼科
数据科学数据科学数据科学
生物医学工程生物医学工程

背景情况:

长视角膜折射手术旨在恢复近视和远视.
由于固有的权衡因素,预测视觉结果仍然具有挑战性.
个性化手术规划对于优化患者满意度至关重要.

研究的目的:

开发术后视力敏度 (近距离,远距离,单眼,双眼) 的预测模型.
创建一个复合的结果指标,距离校正视觉增益 (DCVG).
确定影响长视角膜折射手术结果的关键因素.

主要方法:

对457名接受PresbyMAX治疗的患者的914只眼睛进行了回顾性分析.
机器学习算法的应用 (随机森林) 用于预测视觉敏度.
使用SHAP和PDPs评估特征重要性.

主要成果:

双眼预测模型显示了比单眼模型更高的准确性.
纠正距离视敏度 (CDVA) 比距离纠正近视敏度 (DCNVA) 更容易预测.
为平衡视力的最佳计划补充是+1.50 D到+2.25 D;年龄在45-56岁的患者有更好的近视潜力.

结论:

预测建模和DCVG指标可以实现个性化外科手术规划.
这种方法有助于管理长视眼手术中的视觉权衡.
机器学习促进了个性化的决策,以获得更好的患者咨询和优化的视觉结果.

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