Huang Ziyi, Zhao Xiaowei, Ziv Ohad, Laurita Kenneth R, Rollins Andrew M, Hendon Christine P
Department of Electrical Engineering, Columbia University, New York, NY, USA.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Biomed Opt Express. 2023 Feb 23;14(3):1228-1242. doi: 10.1364/BOE.480943. eCollection 2023 Mar 1.
Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..
射频消融术（RFA）是一种常用于治疗心房颤动的微创手术。然而，由于缺乏对心脏基质的直接可视化以及关于消融灶透壁性的实时反馈，它与心律失常复发和并发症的显著风险相关。在本手稿中，我们提出了一种用于猪左心房心内光学相干断层扫描（OCT）分析的自动化深度学习框架。我们的模型可以准确识别心脏基质，监测导管与组织接触的稳定性，并在OCT强度和偏振敏感OCT数据上评估病灶透壁性。据我们所知，我们开发了首个用于心脏OCT分析的自动框架，有望对心脏RFA治疗进行实时监测和指导。
