Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.
By S. Al’Aref, K. Anchouche, Gurpreet Singh, Piotr J. Slomka, K. Kolli, Amit Kumar, Mohit Pandey, Gabriel Maliakal, A. V. van Rosendael, A. Beecy and 12 more. European Heart Journal, 2018
443 citations recorded by the source corpus.
Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.
Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/e12685a856abbeacf87404fe4460c5c4146f4caf
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging (2023, methodology): “For instance, ML algorithms can merge perfusion data from MRI with coronary anatomy from CT, generating sophisticated 3D heart models, thus improving the detection of cardiac ischaemia and facilitate precise procedural planning [88,89].”