Significance: 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. Aim: 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. Approach: 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. Results: 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. Conclusions: 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.

中文翻译：

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使用机器学习和光散射光谱进行心脏组织表征

意义：心脏组织成分的非破坏性表征为计划和评估手术干预（如消融程序）的有效性提供了重要信息。尽管几种组织表征方法（例如光学相干断层扫描和光纤共聚焦显微镜）显示出前景，但存在许多降低有效性或阻止采用的障碍，例如分析时间延迟、成本过高和应用范围有限。开发一种快速、低成本的非破坏性心脏组织表征方法可以改进心脏外科手术的规划、实施和评估。目的：确定通过神经网络分析光谱的新光散射光谱 (LSS) 系统是否能够预测心室组织的核密度 (ND)。方法：我们开发了一个带有光纤探头的 LSS 系统，并将其应用于来自绵羊模型的心脏组织的测量。我们使用荧光标记、共聚焦显微镜和图像处理量化了心脏组织中的 ND。使用光谱聚类和卷积神经网络 (CNN) 分析从相同心脏组织获得的光谱，以评估通过 LSS 表征组织 ND 的可行性。结果：光谱聚类揭示了与 ND 范围相关的不同光谱组。CNN 将三组光谱分为低、中或高 ND，精度为 95.00 ± 11。77%（平均值和标准偏差）。我们的分析揭示了分类精度对波长范围和光谱二次采样的敏感性。结论：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，例如肥大和纤维化。