Dataset Information

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

ABSTRACT: Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

SUBMITTER: Leiner T

PROVIDER: S-EPMC6778980 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Leiner Tim T Rueckert Daniel D Suinesiaputra Avan A Baeßler Bettina B Nezafat Reza R Išgum Ivana I Young Alistair A AA

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20191007 1

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluat ...[more]

PMID: 31590664

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Project description:BackgroundAtrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning.MethodsNine thousand four hundred forty-eight patients referred for CMR imaging were enrolled and followed over a 5-year period. Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival forests (RSFs) were used to predict new-onset AF and compared to Cox proportional-hazard (CPH) models. The best performing features were identified from 115 variables sourced from three data domains: (i) CMR-based disease phenotype, (ii) patient health questionnaire, and (iii) electronic health records. We evaluated discriminative performance of optimized models using C-index and time-dependent AUC (tAUC).ResultsA RSF-based model of 20 variables (CIROC-AF-20) delivered an overall C-index of 0.78 for the prediction of new-onset AF with respective tAUCs of 0.80, 0.79, and 0.78 at 1-, 2- and 3-years. This outperformed a novel CPH-based model and historic AF risk scores. At 1-year of follow-up, validation cohort patients classified as high-risk of future AF by CIROC-AF-20 went on to experience a 17.3% incidence of new-onset AF, being 24.7-fold higher risk than low risk patients.ConclusionsUsing phenotypic data available at time of CMR imaging we developed and validated the first described risk model for the prediction of new-onset AF in patients with cardiovascular disease. Complementary value was provided by variables from patient-reported measures of health and the electronic health record, illustrating the value of multi-domain phenotypic data for the prediction of AF.

Project description:BACKGROUND:Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS:A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS:Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was <?0.01?min/case (1.2?min for entire dataset); manual segmentation required 3.96?±?0.36?min/case (12.5?h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r?=?0.99, p?<?0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85?±?1.80 vs. 3.33?±?3.18?mL, p?<?0.01): Nearly all (98%) of cases differed by ?5?mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r?=?0.63, p?<?0.001) and yielded small differences with cine-CMR stroke volume (? 1.3?±?17.7?mL, p?=?0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (? 12.6?±?20.9?mL, p?=?0.005), further supporting validity of this method. Among the external validation cohort (n?=?80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (? 1.39?±?1.77?mL, p?=?0.4) and high correlations (r?=?0.99, p?<?0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (? 1.71?±?2.25?mL, p?=?0.25). CONCLUSION:Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Dataset Information

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

Publications

Machine learning in cardiovascular magnetic resonance: basic concepts and applications.

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