Key result
An automated deep learning framework for in vivo intracardiac OCT analysis accurately identified cardiac substrates, monitored contact stability, and assessed lesion transmurality in swine left atria.
Why the study?
RFA for atrial fibrillation carries risks of arrhythmia recurrence and complications due to lack of direct visualization of cardiac substrates and real-time feedback on lesion transmurality.
Does an automated deep learning framework for OCT analysis accurately identify cardiac substrates and assess lesion transmurality in swine left atria?
Does an automated deep learning framework for OCT analysis accurately identify cardiac substrates and assess lesion transmurality in swine left atria?
An automated deep learning framework for in vivo cardiac OCT analysis can accurately assess lesion transmurality and catheter contact in a swine model, showing promise for real-time guidance of radiofrequency ablation.
Demonstrates feasibility in swine models; leaves open translation to human AF ablation procedures.
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 in vivo 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 in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..
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Huang et al. (2023) studied Atrial fibrillation. Automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis was evaluated on Identification of cardiac substrates, monitoring of catheter-tissue contact stability, and assessment of lesion transmurality. An automated deep learning framework for in vivo intracardiac OCT analysis accurately identified cardiac substrates, monitored contact stability, and assessed lesion transmurality in swine left atria.
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