Artificial Intelligence in Cardiovascular Imaging
| Autor: | Lisa J. Lim, Geoffrey H. Tison, Francesca N. Delling |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Disease detection
business.industry Imaging chain Human error Automated segmentation General Medicine Review Multimodal Imaging Multimodality Machine Learning Cardiac Imaging Techniques Workflow ComputingMethodologies_PATTERNRECOGNITION Cardiovascular Diseases Predictive Value of Tests Image Interpretation Computer-Assisted Medicine Humans Artificial intelligence Diagnosis Computer-Assisted Medical diagnosis business Cardiac imaging |
| Zdroj: | Methodist Debakey Cardiovasc J |
| ISSN: | 1947-6108 |
| Popis: | The number of cardiovascular imaging studies is growing exponentially, and so is the need to improve clinical workflow efficiency and avoid missed diagnoses. With the availability and use of large datasets, artificial intelligence (AI) has the potential to improve patient care at every stage of the imaging chain. Current literature indicates that in the short-term, AI has the capacity to reduce human error and save time in the clinical workflow through automated segmentation of cardiac structures. In the future, AI may expand the informational value of diagnostic images based on images alone or a combination of images and clinical variables, thus facilitating disease detection, prognosis, and decision making. This review describes the role of AI, specifically machine learning, in multimodality imaging, including echocardiography, nuclear imaging, computed tomography, and cardiac magnetic resonance, and highlights current uses of AI as well as potential challenges to its widespread implementation. |
| Databáze: | OpenAIRE |
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