非侵入性口腔高光谱成像驱动的心力衰竭数字诊断与保存的喷射部分:模型开发和验证研究研究
Xiaomeng Yang1,2,3, Zeyan Li1,2,3, Lei Lei4
1Cardiovascular Hospital, Renmin Hospital of Wuhan University, Wuhan, China.
这项研究开发了一种非侵入性口腔诊断模型,使用高光谱成像和机器学习来识别心力衰竭与保留射出分数 (HFpEF). 随机森林模型实现了高准确性,使HFpEF的早期检测和改善患者护理成为可能.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 生物医学成像技术 生物医学成像技术
- 计算医学是一种计算医学.
背景情况:
- 口腔微环境障碍与心力衰竭增加的风险有关,其中存在保留喷射分数 (HFpEF) 的风险.
- 超光谱成像 (HSI) 提供了一种非侵入性的方法来检测微妙的生理变化.
研究的目的:
- 开发和验证HFpEF的数字,非侵入性口腔诊断模型.
- 使用HSI结合机器学习算法来检测HFpEF.
主要方法:
- 从175名参与者的口腔高光谱图像中收集了光谱和纹理特征 (140名培训/内部测试,35名外部测试).
- 评估了28个机器学习算法,选择了HFpEF诊断中表现最好的模型 (随机森林).
- 采用SHAP分析来确定特征的重要性和模型可解释性.
主要成果:
- 随机森林模型以高准确度达到AUC为0.884 (内部) 和0.812 (外部).
- SHAP分析证实了HFpEF患者和对照组之间明显的光谱和纹理差异.
- 该模型在识别跨独立数据集的HFpEF方面表现强.
结论:
- 一个非侵入性的,高效的基于HSI的模型可以准确地识别HFpEF的个体.
- 这种诊断框架支持HFpEF的早期检测,诊断和治疗.
- 经过验证的模型显示了提高临床患者护理的巨大潜力.
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