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- Title
Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning.
- Authors
Davies, Rhodri H.; Augusto, João B.; Bhuva, Anish; Xue, Hui; Treibel, Thomas A.; Ye, Yang; Hughes, Rebecca K.; Bai, Wenjia; Lau, Clement; Shiwani, Hunain; Fontana, Marianna; Kozor, Rebecca; Herrey, Anna; Lopes, Luis R.; Maestrini, Viviana; Rosmini, Stefania; Petersen, Steffen E.; Kellman, Peter; Rueckert, Daniel; Greenwood, John P.
- Abstract
Background: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. Methods: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). Findings: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. Conclusion: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision.
- Subjects
CARDIOVASCULAR system physiology; VENTRICULAR ejection fraction; ANTHROPOMETRY; MAGNETIC resonance imaging; MACHINE learning; CARDIOVASCULAR system; HEART ventricles; HEART physiology; ALGORITHMS
- Publication
Journal of Cardiovascular Magnetic Resonance (BioMed Central), 2022, Vol 24, Issue 1, p1
- ISSN
1532-429X
- Publication type
Article
- DOI
10.1186/s12968-022-00846-4