ArticleJournal of cardiovascular development and disease2024
Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images.
Article in Journal of cardiovascular development and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Impact of fully-automated AI based CT-analysis on pre-procedural TAVI planning.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams.Scientific reports · 2026Article
- Uncertainty-Guided Active Learning for Access Route Segmentation and Planning in Transcatheter Aortic Valve Implantation.Journal of imaging · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy. MATERIALS AND
methodsA dataset of 32 contrast-enhanced cardiac CT scans was analyzed. The segmentation approach utilized the DeepLabv3+ model, while the object detection approach employed YOLOv2. The dataset was augmented through rotation and scaling, and five-fold cross-validation was applied. The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared.
resultsThe object detection approach achieved a mean DSC of 0.809, significantly outperforming the segmentation approach, which had a mean DSC of 0.711. Object detection also demonstrated higher precision and recall, with fewer false positives and negatives. The aortic valve annulus area estimation had a mean error of 2.55 mm.
conclusionsObject detection showed superior performance in identifying the aortic valve annulus region, suggesting its potential for clinical application in cardiac imaging. The results highlight the promise of deep learning in improving the accuracy and efficiency of preoperative planning for cardiovascular interventions.
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Registered trials
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