Department of Biomedical Engineering, University of Utah, 36 South Wasatch Drive, Salt Lake City, UT 84112, USA.
Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, 95 S 2000 E, Salt Lake City, UT 84112, USA.
Sensors (Basel). 2021 Sep 9;21(18):6033. doi: 10.3390/s21186033.
Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500-1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions.
光散射光谱学（LSS）是一种成熟的光学方法，可用于生物组织的特征描述。在这里，我们研究了 LSS 和卷积神经网络（CNN）定量描述心脏组织成分和排列的能力。我们使用类似于左房游离壁厚度的固定心肌和主动脉壁组装组织构建体。主动脉切片代表纤维化组织。这些纤维化嵌体的深度、体积分数和排列方式有所不同。我们从相对于光源的多个位置收集构建体的 500-1100nm 波长的光谱。我们使用单条和两条光谱的组合来训练 CNN。使用独立测量的光谱，我们评估了 CNN 对单条和组合光谱分类组织构建体的准确性。包括来自照明纤维远端的纤维的组合光谱通常产生最高的准确性。深度检测、体积分数和排列的最大分类准确性分别为（平均值±标准偏差（stddev））88.97±2.49%、76.33±1.51%和 84.25±1.88%。我们的研究表明，使用 LSS 和 CNN 的组合可以可靠地对组织成分和排列进行定量描述。该方法的潜在临床应用包括术中对心房纤维化的定量和绘图，以及对消融损伤的评估。
