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基于人工神经网络的射频消光中热损伤的预测模型

Tong Ren¹, Yuqi Wu¹, Xiaomei Wu^1,2,3,4,5

¹Department of Biomedical Engineering, School of information Science and Technology, Fudan University, Shanghai, 200433, China.

Cardiovascular engineering and technology

|June 30, 2025

概括

这项研究开发了一种人工神经网络 (ANN) 模型,用于预测射频心脏剥离 (RFCA) 期间的热损伤大小. 该模型准确预测病变尺寸,提高治疗精度和患者的结果.

科学领域:

心血管干预心血管干预
医疗人工智能医疗人工智能
生物医学工程生物医学工程

背景情况:

射频心脏移除 (RFCA) 是心房动 (AF) 的关键治疗方法.
精确控制热损伤大小对于RFCA的有效性和安全性至关重要.
人工智能,特别是神经网络,在预测复杂的生物过程方面表现有前途.

研究的目的:

开发一种人工神经网络 (ANN) 模型,用于预测RFCA过程中产生的热损伤的深度,宽度和体积.
通过精确的损伤大小预测,提高RFCA程序的精度和安全性.

主要方法:

设计了一个双分支的人工神经网络 (ANN) 模型.
该模型使用射频功率,切除时间,导管-组织接触力和接触角度作为输入参数.
训练数据是使用有限元模型生成的,验证是使用ex vivo猪实验进行的.

主要成果:

有限元模型成功模拟了热损伤的形成,提供了强大的训练数据.
活体实验为模型测试提供了可靠的数据.
该ANN模型在预测损伤深度 (误差0.1986毫米),宽度 (误差0.7891毫米) 和体积 (误差4.9384毫米3) 方面取得了很高的准确性.

结论:

一种新的双分支ANN模型提供了一种高效可靠的方法来预测RFCA病变大小.
与现有模型相比,拟议的ANN模型显示出优越的预测能力.
这种工具可以显著提高RFCA程序的控制和成功率.

关键词:

人工神经网络的人工神经网络一个ex vivo实验的实验.有限元素分析的研究.无线电频率心脏除术热损伤的大小热损伤大小

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