Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence
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 inve...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Interventional Cardiology |
| Online Access: | http://dx.doi.org/10.1155/2022/5797431 |
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| author | Kilian Michiels Eva Heffinck Patricio Astudillo Ivan Wong Peter Mortier Alessandra Maria Bavo |
| author_facet | Kilian Michiels Eva Heffinck Patricio Astudillo Ivan Wong Peter Mortier Alessandra Maria Bavo |
| author_sort | Kilian Michiels |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-993fc5d72e7f44b2af61747e3e8b94c1 |
| institution | Kabale University |
| issn | 1540-8183 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Interventional Cardiology |
| spelling | doaj-art-993fc5d72e7f44b2af61747e3e8b94c12025-02-03T06:07:34ZengWileyJournal of Interventional Cardiology1540-81832022-01-01202210.1155/2022/5797431Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial IntelligenceKilian Michiels0Eva Heffinck1Patricio Astudillo2Ivan Wong3Peter Mortier4Alessandra Maria Bavo5FEops NVFEops NVFEops NVThe Heart CentreFEops NVFEops NVBackground. 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.http://dx.doi.org/10.1155/2022/5797431 |
| spellingShingle | Kilian Michiels Eva Heffinck Patricio Astudillo Ivan Wong Peter Mortier Alessandra Maria Bavo Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence Journal of Interventional Cardiology |
| title | Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence |
| title_full | Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence |
| title_fullStr | Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence |
| title_full_unstemmed | Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence |
| title_short | Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence |
| title_sort | automated msct analysis for planning left atrial appendage occlusion using artificial intelligence |
| url | http://dx.doi.org/10.1155/2022/5797431 |
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