|
|||||
|
|
Machine learning in cardiac CT: Basic concepts and contemporary data |
|
| Institution: | 1. Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, USA;2. Division of Cardiovascular Imaging, Medical University of South Carolina, Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Charleston, SC, USA;3. University of Groningen, University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, Groningen, The Netherlands;1. Department of Cardiology, Friedrich-Alexander-Universität, Germany;2. Department of Cardiac Surgery, Friedrich-Alexander-Universität, Germany;1. Cardiac MR PET CT Program, Massachusetts General Hospital, Harvard Medical School, Boston, USA;2. Department of Diagnostic and Interventional Radiology, University of Leipzig, Heart Center, Leipzig, Germany;3. Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Harvard Medical School, Boston, USA;4. Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA;5. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, USA |
| Abstract: | Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations. |
| Keywords: | Machine learning Computed tomography Coronary artery calcium Diagnostic performance ML"} {"#name":"keyword" "$":{"id":"kwrd0035"} "$$":[{"#name":"text" "_":"machine learning AUC"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"area under the curve SSS"} {"#name":"keyword" "$":{"id":"kwrd0055"} "$$":[{"#name":"text" "_":"segment stenosis score EFV"} {"#name":"keyword" "$":{"id":"kwrd0065"} "$$":[{"#name":"text" "_":"epicardial fat volume CTP"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"CT perfusion KNN"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"K nearest neighbor PVE"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"partial volume effect CAC"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"coronary artery calcium MSCCTA"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"multislice CCTA CSCT"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"cardiac calcium scoring CT ICC"} {"#name":"keyword" "$":{"id":"kwrd0135"} "$$":[{"#name":"text" "_":"intraclass correlation QCA"} {"#name":"keyword" "$":{"id":"kwrd0145"} "$$":[{"#name":"text" "_":"quantitative coronary angiography FFR"} {"#name":"keyword" "$":{"id":"kwrd0155"} "$$":[{"#name":"text" "_":"fractional flow reserve LD-NCP"} {"#name":"keyword" "$":{"id":"kwrd0165"} "$$":[{"#name":"text" "_":"low density non-calcified plaque DSC"} {"#name":"keyword" "$":{"id":"kwrd0175"} "$$":[{"#name":"text" "_":"Dice score coefficients IQR"} {"#name":"keyword" "$":{"id":"kwrd0185"} "$$":[{"#name":"text" "_":"interquartile range |
| 本文献已被 ScienceDirect 等数据库收录！ | |