Cardiac computed tomography for the assessment of myocardial bridging: a scoping review of the emerging role of artificial intelligence and machine learning
Abu Suleiman, Amro Russo, Federico Della Valle, Luigi Ausiello, Davide Bukowska-Olech, Ewelina Iannibelli, Vincenzo Al Droubi, M Omar Sannino, Gabriella Bernardi, Marco Spadafora, Luigi
Abu Suleiman, Amro
Russo, Federico
Della Valle, Luigi
Ausiello, Davide
Bukowska-Olech, Ewelina
Iannibelli, Vincenzo
Al Droubi, M Omar
Sannino, Gabriella
Bernardi, Marco
Spadafora, Luigi
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Affiliation
Hull University Teaching Hospitals; Sapienza University of Rome; Sant' Andrea University Hospital; Poznan University of Medical Sciences; Policlinico Tor Vergata; Walsall Healthcare NHS Trust; Catholic University of the Sacred Heart; Santa Maria Goretti Hospital
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Publication date
2025-09-12
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Abstract
(1) Background: Myocardial bridging (MB) is a congenital coronary anomaly with potential clinical significance. Artificial intelligence (AI) applied to cardiac computed tomography angiography (CCTA), particularly through CT-derived fractional flow reserve (CT-FFR), offers a novel, non-invasive approach for assessing MB. (2) Methods: We conducted a systematic review of the literature focusing on studies investigating AI-enhanced CCTA in the evaluation of MB. (3) Results: Ten studies were included. AI-based models, including radiomics, demonstrated moderate to high accuracy in predicting proximal plaque formation, and motion correction algorithms improved image quality and diagnostic confidence. Other findings were limited by the types of studies included and conflicting findings across studies. (4) Conclusions: AI-enhanced CCTA shows promise for the non-invasive functional assessment of MB and its risk stratification. Further prospective studies and validation are required to establish standardized protocols and confirm clinical utility.
Citation
Abu Suleiman A, Russo F, Della Valle L, Ausiello D, Bukowska-Olech E, Iannibelli V, Al Droubi MO, Sannino G, Bernardi M, Spadafora L. Cardiac Computed Tomography for the Assessment of Myocardial Bridging: A Scoping Review of the Emerging Role of Artificial Intelligence and Machine Learning. J Cardiovasc Dev Dis. 2025 Sep 12;12(9):350. doi: 10.3390/jcdd12090350. PMID: 41002629; PMCID: PMC12470274.
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Article