基于物理学的神经网络用于快速预测癌症乳房的温度分布,作为潜在的高效便携式基于人工智能的诊断工具
Olzhas Mukhmetov1, Yong Zhao1, Aigerim Mashekova1
1School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.
Computer methods and programs in biomedicine
|October 18, 2023
概括
一种新的物理信息神经网络 (PINN) 方法模拟了乳腺癌模型中的热传递速度,比传统方法快12倍. 这种人工智能工具提供了一个非侵入性的方法,用于在自我检查期间早期检测乳腺癌.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 计算式热传输是一种热传输技术.
背景情况:
- 精确的乳腺组织热建模对于早期癌症检测至关重要.
- 像有限元分析 (FEA) 这样的传统方法可以是计算密集的.
- 开发更快,非侵入性诊断工具是全球卫生优先事项.
研究的目的:
- 开发和验证一个新的物理信息神经网络 (PINN) 以快速模拟癌症乳腺模型中的热传递.
- 评估PINN作为识别瘤异常区域的工具的潜力.
- 为了比较PINN模拟的速度和准确性与传统的有限元分析 (FEA).
主要方法:
- 实施了一个PINN,将深度学习与热传递的物理原理相结合.
- 使用传热方程余量和边界条件训练PINN,并结合了表面热成像数据.
- 通过将模拟结果与各种乳腺癌情景的FEA进行比较,验证了PINN模型.
主要成果:
- 该PINN方法准确地预测了乳腺组织中的温度分布.
- 皮恩模拟的速度大约是FEA的12倍.
- 该模型显示了识别与瘤相关的热异常的潜力.
结论:
- PINN为乳腺癌诊断中的热建模提供了一个准确且显著更快的替代方案.
- 这种人工智能驱动的方法提供了一种非侵入性和更安全的乳房自我检查方法.
- 开发的技术显示出用于早期乳腺癌检测的便携式人工智能工具的前景,符合世卫组织的建议.
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