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TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning

Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated d...

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Autores principales: Santaló-Corcoy, Marcel, Corbin, Denis, Tastet, Olivier, Lesage, Frédéric, Modine, Thomas, Asgar, Anita, Ben Ali, Walid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606167/
https://www.ncbi.nlm.nih.gov/pubmed/37892002
http://dx.doi.org/10.3390/diagnostics13203181
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author Santaló-Corcoy, Marcel
Corbin, Denis
Tastet, Olivier
Lesage, Frédéric
Modine, Thomas
Asgar, Anita
Ben Ali, Walid
author_facet Santaló-Corcoy, Marcel
Corbin, Denis
Tastet, Olivier
Lesage, Frédéric
Modine, Thomas
Asgar, Anita
Ben Ali, Walid
author_sort Santaló-Corcoy, Marcel
collection PubMed
description Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. Results: High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90–0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. Conclusions: TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures.
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spelling pubmed-106061672023-10-28 TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning Santaló-Corcoy, Marcel Corbin, Denis Tastet, Olivier Lesage, Frédéric Modine, Thomas Asgar, Anita Ben Ali, Walid Diagnostics (Basel) Article Background: Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. Methods: This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. Results: High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90–0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. Conclusions: TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures. MDPI 2023-10-11 /pmc/articles/PMC10606167/ /pubmed/37892002 http://dx.doi.org/10.3390/diagnostics13203181 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Santaló-Corcoy, Marcel
Corbin, Denis
Tastet, Olivier
Lesage, Frédéric
Modine, Thomas
Asgar, Anita
Ben Ali, Walid
TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title_full TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title_fullStr TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title_full_unstemmed TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title_short TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning
title_sort tavi-prep: a deep learning-based tool for automated measurements extraction in tavi planning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606167/
https://www.ncbi.nlm.nih.gov/pubmed/37892002
http://dx.doi.org/10.3390/diagnostics13203181
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