ArticleJournal of interventional cardiology2020
Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography.
Article in Journal of interventional cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 35 citations in OpenAlex.
- Validation of an Automated Artificial Intelligence-Driven Planner for Transcatheter Aortic Valve Replacement.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Article
- AI-based pulmonary artery to ascending aorta ratio on non-contrast CT for pulmonary hypertension: diameter vs. volume assessment.European heart journal. Imaging methods and practice · 2026Article
- Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection.Radiology advances · 2026Article
- Fully Automated Aortic Root Localization and Tilt Alignment in Cardiac Computed Tomography.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- State-of-the-art artificial intelligence methods for pre-operative planning of cardiothoracic surgery and interventions: a narrative review.Journal of thoracic disease · 2025Review
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Automated and quantitative assessment of aortic root based on cardiac computed tomography angiography using a new deep-learning tool: a comparison study.Quantitative imaging in medicine and surgery · 2024Article
- Validation Study of Two Artificial Intelligence-Based Preplanning Methods for Transcatheter Aortic Valve Replacement Procedures.Journal of the Society for Cardiovascular Angiography & Interventions · 2024Article
- Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.Frontiers in cardiovascular medicine · 2024Review
- Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis.Reviews in cardiovascular medicine · 2024Review
- TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning.Diagnostics (Basel, Switzerland) · 2023Article
- Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.Journal of Korean medical science · 2023Article
- Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence.Journal of interventional cardiology · 2022Article
- Patient-specific Computer Simulation: An Emerging Technology for Guiding the Transcatheter Treatment of Patients with Bicuspid Aortic Valve.Interventional cardiology (London, England) · 2021Review
- Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography angiography before transcatheter aortic valve replacement.Therapeutic advances in cardiovascular diseaseArticle
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1-2.1], 2.0 mm [1.3-2.8] with a paired difference -0.5 ± 1.3 mm and
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.