一种机器学习方法来预测眼过手术的结果
Luca Agnifili1, Michele Figus2, Annamaria Porreca3
1Department of Medicine and Ageing Science, Ophthalmology Clinic, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini, 66100, Chieti, CH, Italy. l.agnifili@unich.it.
Scientific reports
|October 24, 2023
概括
机器学习可以准确地预测过手术的结果. 结膜肌层的厚度和反射性,以及患者的年龄,是青光眼患者成功或失败的关键指标.
科学领域:
- 眼科医生 眼科 眼科
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 青光眼过手术 (FS) 的成功率各不相同,需要预测模型.
- 精确预测FS结果可以优化患者管理和手术规划.
- 目前用于预测FS结果的方法缺乏足够的准确性.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测青光眼患者的过手术 (FS) 结果.
- 确定影响FS成功的关键手术前参数.
- 使用分类树 (CT) 算法进行结果预测.
主要方法:
- 分析了接受FS的102名青光眼患者.
- 评估了眼表面临床测试 (OSCT),手术部位相关的生物识别参数 (SSP) 和结膜参数.
- 前段光学连贯性断层扫描 (AS-OCT) 测量了结膜上皮和肌层厚度 (CET,CST) 和反射率 (ECR,SCR).
- 使用分类树 (CT) ML算法进行数据分析.
主要成果:
- 过手术的成功率在12个月后为60.8%.
- 结膜层层厚度 (CST) 和反射率 (SCR),以及年龄,是FS结果的最重要的预测因素.
- 确定了CST,SCR和年龄的截止值,以预测成功/失败.
- ML模型实现了0.784的AUC,表明了良好的预测准确性.
结论:
- 机器学习,特别是CT分析,可以准确预测过手术的结果.
- 手术前的结膜层状瘤厚度和反射性,以及患者的年龄,是FS成功的重要预测因素.
- 较厚的,高反射性结膜层和年轻的年龄与FS衰竭的风险增加有关.
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