Lots of interesting abstracts and cases were submitted for TCTAP 2025. Below are the accepted ones after a thorough review by our official reviewers. Don’t miss the opportunity to expand your knowledge and interact with authors as well as virtual participants by sharing your opinion in the comment section!
TCTAP A-098
Fully Automated Aortic Root Localization and Tilt Alignment in Cardiac Computed Tomography
By Vinayak Nagaraja, Elham Mahmoudi, Mohammad Sarraf, Paul Friedman, Mohamad Alkhouli, Mackram Eleid, Mandeep Singh, Zachi Attia, Sanaz Vahdati, Joseph Sobeck, Mohammadreza Naderian, Fred Nugen, Bardia Khosravi, Bradley Erickson
Presenter
Vinayak Nagaraja
Authors
Vinayak Nagaraja1, Elham Mahmoudi1, Mohammad Sarraf1, Paul Friedman1, Mohamad Alkhouli1, Mackram Eleid1, Mandeep Singh1, Zachi Attia1, Sanaz Vahdati1, Joseph Sobeck1, Mohammadreza Naderian1, Fred Nugen1, Bardia Khosravi1, Bradley Erickson1
Affiliation
Mayo Clinic, USA1
View Study Report
TCTAP A-098
Digital Health and Artificial Intelligence
Fully Automated Aortic Root Localization and Tilt Alignment in Cardiac Computed Tomography
Vinayak Nagaraja1, Elham Mahmoudi1, Mohammad Sarraf1, Paul Friedman1, Mohamad Alkhouli1, Mackram Eleid1, Mandeep Singh1, Zachi Attia1, Sanaz Vahdati1, Joseph Sobeck1, Mohammadreza Naderian1, Fred Nugen1, Bardia Khosravi1, Bradley Erickson1
Mayo Clinic, USA1
Background
Automated analysis of cardiac computed tomography (CCT) studies may help in personalized management and outcome prediction of patients undergoing Transcatheter aortic valve replacement (TAVR). This study aims to develop an object detection model and propose statistical algorithms to localize aortic root aligned with its tilt angle.
Methods
All consecutive patients who underwent CCT for TAVR procedure, from January to July 2023 at our center with no prior aortic valve prosthesis or permanent pacemaker were retrospectively included. The baseline annotations were performed by two experts. For object detection, MedYOLO, an adaptation of YOLO models for 3D volumetric data, was fine-tuned on the training set and its performance was evaluated by recall, precision, F1 and average precision at an IoU overlap of 50% (mAP50) and mAP50-95 on an unseen test set. For tilt alignment, intensity thresholding, connected component and principal component analysis were proposed and evaluated by Bland-Altman comparison.
Results
Out of 179 CCTs, 100 CCTs were assigned to the test set, and the remaining to the training and validation using a 4:1 split. The model detected the aortic root with recall, precision and F1 score of 99.0%, respectively, mAP50 of 99.5% and mAP50-95 of 60.4%. The tilt prediction algorithm had a mean error of 7.9 [(-5.3)–21.1] degrees compared to the actual measurements and 3.3 [(-6.7)-13.4] between observers.
Conclusion
This study demonstrates the robust performance of a fully automated pipeline for detection and analysis of key features in pre-TAVR CCTs. Further prospective studies are required for clinical developments.