Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment: The Case of Computed Tomography Fractional Flow Reserve

• Published in: Journal of thoracic imaging Vol. 35 Suppl 1; p. S66
• Main Authors: Tesche, Christian, Gray, Hunter N
• Format: Journal Article
• Language: English
• Published: United States 01.05.2020
• Subjects: Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary Artery Disease - diagnostic imaging; Coronary Vessels - diagnostic imaging; Fractional Flow Reserve, Myocardial; Humans; Machine Learning; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted - methods
• ISSN: 1536-0237
• DOI: 10.1097/RTI.0000000000000483

Coronary computed tomography angiography (cCTA) is a reliable and clinically proven method for the evaluation of coronary artery disease. cCTA data sets can be used to derive fractional flow reserve (FFR) as CT-FFR. This method has respectable results when compared in previous trials to invasive FFR, with the aim of detecting lesion-specific ischemia. Results from previous studies have shown many benefits, including improved therapeutic guidance to efficiently justify the management of patients with suspected coronary artery disease and enhanced outcomes and reduced health care costs. More recently, a technical approach to the calculation of CT-FFR using an artificial intelligence deep machine learning (ML) algorithm has been introduced. ML algorithms provide information in a more objective, reproducible, and rational manner and with improved diagnostic accuracy in comparison to cCTA. This review gives an overview of the technical background, clinical validation, and implementation of ML applications in CT-FFR.