一个基于多物理的人工神经网络模型用于动脉样硬化
M Soleimani1, B Dashtbozorg2, M Mirkhalaf3
1Institute of Continuum Mechanics, Leibniz Universität Hannover, Hannover, Germany.
Heliyon
|July 24, 2023
概括
人工神经网络 (ANN) 显著加快了用于预测动脉样硬化的计算模型. 这种机器学习方法为传统方法提供了一个计算效率高的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 医学物理 医学物理
背景情况:
- 动脉样硬化,以动脉壁硬化为特征,对健康构成重大风险.
- 有预测性的数学多物理模型,但它们是计算密集的.
- 提高这些模型的效率对于更广泛的应用至关重要.
研究的目的:
- 提高动脉样硬化发展预测模型的计算效率.
- 为此目的,利用机器学习,特别是人工神经网络 (ANN).
- 开发一种更快,更准确的动脉样硬化预测工具.
主要方法:
- 使用多物理有限元法 (FEM) 模拟创建一个全面的数据库.
- 使用FEM模拟数据进行人工神经网络 (ANN) 模型的培训和验证.
- 与传统的FEM模拟相比,ANN模型性能的比较.
主要成果:
- 开发的ANN模型在预测动脉样硬化进展方面表现出高准确度.
- 使用ANN模型实现了大量的计算效率提升.
- 该ANN模型提供了快速预测,克服了传统FEM模拟的局限性.
结论:
- 人工神经网络为预测动脉样硬化提供了一种计算效率高,准确的方法.
- 这种机器学习方法可以加速心血管疾病的研究和临床应用.
- 该研究强调了人工智能在增强复杂生物系统建模方面的潜力.
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