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以深度学习为基础,对人类骨肌肉中的毛细血管超结构进行分类

Marius Reto Bigler¹, Oliver Baum²

¹Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Frontiers in molecular biosciences

|May 16, 2024

概括

一个深度学习模型准确地确定了健康个体和患有系统性疾病的人之间的骨肌肉毛细血管的差异. 这种卷积神经网络 (CNN) 方法显示出在毛细血管研究中产生新假设的前景.

科学领域:

使用先进的深度学习技术,特别是卷积神经网络 (CNN),用于生物图像分析.
专注于人类骨肌肉生理学,病理学和计算方法的交集.

背景情况:

人类骨肌中的毛细血管超结构容易受到糖尿病和高血压等系统性疾病引起的变化.
传输电子显微镜 (TEM) 图像的传统形态测量分析量化了毛细血管结构.
深度学习提供了一种新的方法来识别生物数据中的复杂模式.

研究的目的:

训练一个卷积神经网络 (CNN),以检测健康个体和全身病理患者之间的肌肉毛细血管的形态差异.
为了利用一种产生假设的方法,对毛细血管变化的新见解.

主要方法:

一项回顾性研究分析了来自70名参与者的1810张人类骨肌肉毛细血管电子显微镜 (健康对照对照:糖尿病患者,动脉高血压患者或外周动脉疾病患者).
使用预先训练的开放访问CNN (ResNet101) 来区分来自两个组的毛细血管图像.
对CNN的性能进行了手动定量形态测量分析,包括底层膜 (BM) 厚度.

主要成果:

手动分析显示,底层膜 (BM) 厚度在形态指标中具有最高的诊断准确性 (AUC:0.657±0.050).
性能最好的CNN实现了79%的诊断准确性 (灵敏度93%,特异性92%),显著优于BM厚度分析 (p < 0.001).
美国有线电视新闻网 (CNN) 的预测形态主要强调了细胞周围细胞脱bridement.

结论:

与传统的形态分析相比,产生假设的CNN方法在区分健康个体的毛细血管和有系统病理的毛细血管方面表现出卓越的准确性.
这种深度学习方法为发现微妙的超结构差异和在血管病理学中产生新的研究假设提供了强大的工具.

关键词:

毛细血管是毛细血管的组成部分.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.这是骨肌肉的骨架肌肉.传输电子显微镜的使用

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