RESUMO
BACKGROUND: The exclusion/occlusion of the left atrial appendage (LAA) is a treatment option for atrial fibrillation (AF) patients who are at high risk of stroke and high risk of bleeding. As the role of the LAA is not well understood or explored, this study aims to assess its role on flow dynamics in the left atrium. METHODS: Computational fluid dynamics (CFD) simulations were carried out for nine AF patients before and after LAA exclusion. The flow parameters investigated included the LA velocities, Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT), and Pressure in the LA. RESULTS: This study shows that, on average, a decrease in TAWSS (1.82 ± 1.85 Pa to 1.27 ± 0.96 Pa, p < 0.05) and a slight increase in OSI (0.16 ± 0.10 to 0.17 ± 0.10, p < 0.05), RRT (1.87 ± 1.84 Pa-1 to 2.11 ± 1.78 Pa-1, p < 0.05), and pressure (-19.2 ± 6.8 mmHg to -15.3 ± 8.3 mmHg, p < 0.05) were observed in the LA after the exclusion of the LAA, with a decrease in low-magnitude velocities. CONCLUSION: The exclusion of the LAA seems to be associated with changes in LA flow dynamics. Further studies are needed to elucidate the clinical implications of these changes.
RESUMO
Background: The number of multislice computed tomography (MSCT) analyses performed for planning structural heart interventions is rapidly increasing. Further automation is required to save time, increase standardization, and reduce the learning curve. Objective: The purpose of this study was to investigate the feasibility of a fully automated artificial intelligence (AI)-based MSCT analysis for planning structural heart interventions, focusing on left atrial appendage occlusion (LAAO) as the selected use case. Methods: Different deep learning models were trained, validated, and tested using a cohort of 583 patients for which manually annotated data were available. These models were used independently or in combination to detect the anatomical ostium, the landing zone, the mitral valve annulus, and the fossa ovalis and to segment the left atrium (LA) and left atrial appendage (LAA). The accuracy of the models was evaluated through comparison with the manually annotated data. Results: The automated analysis was performed on 25 randomly selected patients of the test cohort. The results were compared to the manually identified landmarks. The predicted segmentation of the LA(A) was similar to the manual segmentation (dice score of 0.94 ± 0.02). The difference between the automatically predicted and manually measured perimeter-based diameter was -0.8 ± 1.3 mm (anatomical ostium), -1.0 ± 1.5 mm (Amulet landing zone), and -0.1 ± 1.3 mm (Watchman FLX landing zone), which is similar to the operator variability on these measurements. Finally, the detected mitral valve annulus and fossa ovalis were close to the manual detection of these landmarks, as shown by the Hausdorff distance (3.9 ± 1.2 mm and 4.8 ± 1.8 mm, respectively). The average runtime of the complete workflow, including data pre- and postprocessing, was 57.5 ± 34.5 seconds. Conclusions: A fast and accurate AI-based workflow is proposed to automatically analyze MSCT images for planning LAAO. The approach, which can be easily extended toward other structural heart interventions, may help to handle the rapidly increasing volumes of patients.
