可解释的深度学习和TMJ障碍的生物力学建模 形态风险因素
Shuchun Sun1, Pei Xu2, Nathan Buchweitz1
1Clemson-MUSC Joint Bioengineering Program, Department of Bioengineering and.
JCI insight
|July 11, 2024
概括
这项研究结合了深度学习和生物机械建模,揭示了关节 (TMJ) 疾病的原因. 研究结果揭示了特定的面部特征如何通过改变关节力学和细胞功能来增加关节障碍风险.
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
- 肌肉骨研究 研究
背景情况:
- 了解像关节 (TMJ) 关节疾病这样的多因素肌肉骨疾病对于有效的预防和治疗至关重要.
- 深度学习擅长识别风险因素,但缺乏用于临床应用的机制性见解.
- 多尺度生物机械建模在生理学背景下提供了机械学的理解.
研究的目的:
- 开发和应用一种混合方法,整合3D可解释深度学习和多尺度生物机械建模.
- 通过识别风险因素及其与关节生物力学的机制联系,调查关节疾病的病因.
- 提高深度学习在病因学研究中的临床适用性.
主要方法:
- 使用3D卷积神经网络,确定与关节疾病相关的患者特异性形态特征.
- 可解释的深度学习输出被用来驱动多尺度生物力学模型.
- 生物机械模型模拟了关节力,组织营养物质的可用性,细胞ATP生产和磁盘应变能量密度.
主要成果:
- 深度学习模型准确地识别了基于状骨,骨和下巴形态的TMJ障碍患者.
- 小下大小和平坦的状形状被确定为关节疾病的危险因素.
- 这些形态因素在机理上与增加的关节力,降低的营养素/ATP水平和增加的磁盘应变能量密度有关.
结论:
- 混合方法成功地将深度学习的风险识别与生物力学建模的机械解释相结合.
- 这种整合解决了深度学习对病因学研究的临床翻译信心的局限性.
- 该方法通过结合必要的生物力学背景,提高了分析较小临床数据集的可访问性.
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