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Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance
- Source :
- The Lancet. Digital health, Vol. 3, no.1, p. e20-e28 (2021)
- Publication Year :
- 2021
-
Abstract
- Background: Left ventricular maximum wall thickness (MWT) is central to diagnosis and risk stratification of hypertrophic cardiomyopathy, but human measurement is prone to variability. We developed an automated machine learning algorithm for MWT measurement and compared precision (reproducibility) with that of 11 international experts, using a dataset of patients with hypertrophic cardiomyopathy. Methods: 60 adult patients with hypertrophic cardiomyopathy, including those carrying hypertrophic cardiomyopathy gene mutations, were recruited at three institutes in the UK from August, 2018, to September, 2019: Barts Heart Centre, University College London Hospital (The Heart Hospital), and Leeds Teaching Hospitals NHS Trust. Participants had two cardiovascular magnetic resonance scans (test and retest) on the same day, ensuring no biological variability, using four cardiac MRI scanner models represented across two manufacturers and two field strengths. End-diastolic short-axis MWT was measured in test and retest by 11 international experts (from nine centres in six countries) and an automated machine learning method, which was trained to segment endocardial and epicardial contours on an independent, multicentre, multidisease dataset of 1923 patients. Machine learning MWT measurement was done with a method based on solving Laplace's equation. To assess test–retest reproducibility, we estimated the absolute test–retest MWT difference (precision), the coefficient of variation (CoV) for duplicate measurements, and the number of patients reclassified between test and retest according to different thresholds (MWT >15 mm and >30 mm). We calculated the sample size required to detect a prespecified MWT change between pairs of scans for machine learning and each expert. Findings: 1440 MWT measurements were analysed, corresponding to two scans from 60 participants by 12 observers (11 experts and machine learning). Experts differed in the MWT they measured, ranging from 14·9 mm (SD 4·2) to 19·0 mm (4·7; p
- Subjects :
- Adult
Male
Coefficient of variation
Heart Ventricles
Medicine (miscellaneous)
Health Informatics
Machine learning
computer.software_genre
Risk Assessment
Machine Learning
Health Information Management
Medicine
Humans
Decision Sciences (miscellaneous)
Aged
Observer Variation
Reproducibility
business.industry
Hypertrophic cardiomyopathy
Reproducibility of Results
Cardiomyopathy, Hypertrophic
Middle Aged
medicine.disease
Magnetic Resonance Imaging
United Kingdom
Test (assessment)
Sample size determination
Risk stratification
Female
Artificial intelligence
Cutoff point
business
Wall thickness
computer
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- The Lancet. Digital health, Vol. 3, no.1, p. e20-e28 (2021)
- Accession number :
- edsair.doi.dedup.....be02630518a4a2633ad809090837811b