Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning.
J Cardiovasc Magn Reson
; 24(1): 16, 2022 03 10.
Article
em En
| MEDLINE
| ID: mdl-35272664
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article