title: Cardiac imaging: working towards fully-automated machine analysis & interpretation.
abstract: Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.
authors.affiliation: a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.affiliation: b Biomedical Imaging Research Institute , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.affiliation: c Philips Research North America , Cambridge , MA , USA.
authors.forename: Arkadiusz
authors.initials: A
authors.lastname: Sitek
authors.affiliation: d Cardiovascular Imaging , Manchester Heart Centre, Manchester Royal Infirmary , Manchester , UK.
authors.forename: Manish
authors.initials: M
authors.lastname: Motwani
authors.affiliation: a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.affiliation: a Department of Imaging (Division of Nuclear Medicine) and Medicine , Cedars-Sinai Medical Center , Los Angeles , CA , USA.
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
mesh_terms: D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D057285:Precision Medicine / Q000295:instrumentation / Q000379:methods; D012984:Software
keywords: Artificial intelligence; cardiac imaging; deep learning; image segmentation; machine learning