| Description |
Current biopsy approaches either lack depth penetration or real-time analysis. Light scattering spectroscopy (LSS) aims to address these issues, and our goal is to investigate the use of LSS for identifying cardiac conduction tissues. LSS data were collected from donor tissue samples and created a 3D heart model by staining the samples, segmenting them using a modified U-Net, and registering them. The model allowed determination of the tissue type and depth under each LSS point. Dimensionality reduction approaches were employed to visualize the data and used statistical methods to assess if key regions varied statistically in tissue composition. Based on this information, labels were generated for the tissue that could be utilized by machine learning algorithms. Various deep learning and machine learning models were tested to determine how LSS could differentiate based on the underlying tissue and if it could identify cardiac conduction tissue. Significant differences were observed in connective tissue, muscle tissue, and nuclei density in nodal regions compared to other regions. The machine learning models identified muscle tissue as the strongest predictor of nodal tissue. LSS shows promise for identifying cardiac conduction tissue and supports previous research in spectroscopy. By continuing to develop the LSS-ML system, we could further enhance tissue classification from optical approaches, which are necessary to advance to clinical trials, ultimately improving patient quality of life and healthcare outcomes for critical procedures. |
