University of Utah, Department of Biomedical Engineering, Salt Lake City, United States.
University of Utah, Nora Eccles Harrison Cardiovascular Research and Training Institute, Salt Lake C, United States.
J Biomed Opt. 2021 Nov;26(11). doi: 10.1117/1.JBO.26.11.116001.
The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures.
To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues.
We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS.
Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of 95.00 ± 11.77 % (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra.
LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.
对心脏组织成分进行非破坏性特征描述可为心脏手术干预（如消融术）的规划和评估提供重要信息。虽然组织特征描述的几种方法，如光学相干断层扫描和光纤共聚焦显微镜，显示出一定的前景，但仍存在许多障碍，例如分析的时间延迟、过高的成本以及应用范围有限，这些都降低了其有效性或阻碍了其应用。开发一种快速、低成本的心脏组织特征描述方法可以改善心脏手术的规划、实施和评估。
确定一种新的基于光散射光谱（LSS）的系统是否能够通过神经网络分析预测心室组织的核密度（ND）。
我们开发了一种带有光纤探头的 LSS 系统，并将其应用于绵羊模型的心脏组织测量。我们使用荧光标记、共聚焦显微镜和图像处理来量化心脏组织中的 ND。使用相同的心脏组织获取的光谱通过光谱聚类和卷积神经网络（CNN）进行分析，以评估通过 LSS 对组织 ND 进行特征描述的可行性。
光谱聚类揭示了与 ND 范围相关的光谱的明显分组。CNN 以 95.00±11.77%（平均值和标准差）的准确率将低、中或高 ND 的三组光谱分类。我们的分析表明，分类准确性对波长范围和光谱子采样的灵敏度。
LSS 和机器学习能够评估心脏组织中的 ND。我们建议该方法可用于诊断与 ND 变化相关的心脏疾病，如肥大和纤维化。
