Abstract 4370347: Comparative Performance Of Al-driven CCTA Models For Pre-TAVR CAD Detection: A Systematic Review

M Muhammad Ahmad Qureshi (Henry Ford Jackson Hospital, Jackson, Michigan, United States) S Shahan Haseeb (Northwell health, Port Jefferson, New York, United States) M Mishita Goel (Wayne State University, Rochester, Michigan, United States) M Mariam Saleem (Henry Ford Jackson Hospital, Jackson, Michigan, United States) D Danyal Bakht (King Edward Medical University, Lahore, Pakistan) K Khawar Ali (King Edward Medical University, Lahore, Pakistan) M Maaz Amir (King Edward Medical University, Lahore, Punjab, Pakistan) K Kartikeya Srivastava (Henry Ford Jackson Hospital, Jackson, Michigan, United States) L Laibah Khan (University of Mississippi Med CT, Jackson, Mississippi, United States) A Affaf shahid (Allama Iqbal Medical College, Bahawalpur, Pakistan) A Aiden Abidov (Wayne State University, West Bloomfield, Michigan, United States)

Abstract

Introduction: Accurate assessment of coronary artery disease (CAD) before Transcatheter Aortic Valve Replacement (TAVR) is crucial for optimal outcomes. Al- enhanced coronary a andiocraphy (CCA shows promise for CAD detection, but its vertormance in TAVR patients lacks systemaic eva uation. his reviev analyzes Al-based CCTA models for pre-TAVR CAD detection focusing on both anatomic and physiologic criteria. Methods: We searched Pubiied and Embase tor studies on Al-based CAD detection via CCIA in pre-TAVR patients. Five studies met inclusion criteria, each using different Al models. Key metrics analyzed included sensitivity, specificity, accuracy, negative predictive value (NPV), positive predictive value (PPV), and Net reclassification index (NRI) for CAD+ve and CAD-ve populations. Results: The studies showed significant variability in Al model performance for CAD detection. Sensitivity ranged from 94.9% to 100%, specificity from 40.0% to 100%, and accuracy from 60.0% to 91.9%. CAD+ve NRI: -0.28 to 0.66 (e.g., Brandt et al. 2022 showed strong positive reclassification). CAD-Ve NAl: 005 to 0.13 (most models showed postive reclassication) Models integrating anatomic and physiological criteria (e.g., Brendel et al. 2024 with Al-based CAD-RADS + Al-derived FFR) achieved superior accuracy (96.8% sensitivity, 100% specificity, 91.9% accuracy, CAD+ve NRI 0.11, CAD-ve NRI 0.07). In contrast, purely anatomic models (e.g., Gohman et al. 2022) had lower performance (52.0% specificity, 67.3% accuracy, CAD +ve NRI -0.05, CAD-ve NRI 0.13). Conclusions: Al-based CCTA models show high sensitivity for CAD detection in pre-TAVR assessment, with varying specificity and accuracy. Combining anatomic and physiological parameters improves diagnostic accuracy and NRI. This review underscores the potential of integrated Al models for more precise pre-TAVR CAD screening, with further research needed for clinical validation.

Article Details

Journal Circulation
Volume / Issue Vol. 152, Issue Suppl_3
Published November 04, 2025
ISSN 0009-7322
Publisher Lippincott Williams & Wilkins

Journal Info

Circulation

Lippincott Williams & Wilkins

ISSN: 0009-7322 Health Sciences

Authors (11)

M

Muhammad Ahmad Qureshi

Henry Ford Jackson Hospital, Jackson, Michigan, United States

S

Shahan Haseeb

Northwell health, Port Jefferson, New York, United States

M

Mishita Goel

Wayne State University, Rochester, Michigan, United States

M

Mariam Saleem

Henry Ford Jackson Hospital, Jackson, Michigan, United States

D

Danyal Bakht

King Edward Medical University, Lahore, Pakistan

K

Khawar Ali

King Edward Medical University, Lahore, Pakistan

M

Maaz Amir

King Edward Medical University, Lahore, Punjab, Pakistan

K

Kartikeya Srivastava

Henry Ford Jackson Hospital, Jackson, Michigan, United States

L

Laibah Khan

University of Mississippi Med CT, Jackson, Mississippi, United States

A

Affaf shahid

Allama Iqbal Medical College, Bahawalpur, Pakistan

A

Aiden Abidov

Wayne State University, West Bloomfield, Michigan, United States