E Machine learning wall thickness measurement in hypertrophic cardiomyopathy exceeds performance of world experts. (17th July 2020) - Augusto, João B; Davies, Rhodri; Bhuva, Anish; Knott, Kristopher; Seraphim, Andreas; Al-Farih, Mashael; Lau, Clement; Hughes, Rebecca; Lopes, Luís; Shiwani, Hunain; Gerber, Bernhard; Craig, Christian H; et al.
Abstract : Background: Left ventricular maximum wall thickness (MWT) is central to diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM), but measurement has variation. Objectives: We developed a fully automated machine learning (ML) algorithm for MWT measurement and compared it to international experts using precision (repeatability) on a dataset of HCM patients scanned twice with cardiovascular magnetic resonance (CMR). Methods: Training dataset : Endo- and epicardial end-diastolic contours were derived using a fully-automated convolutional neural network trained on 1, 923 independent multi-centre multi-disease cases (14 centres from 3 countries, 10 scanner models, 2 field strengths, with balanced pathologies - health, athletes, myocardial infarction, aortic stenosis, HCM, dilated cardiomyopathy, infiltrative diseases) all segmented by a single expert. Patients : 60 HCM patients were scanned twice (scan:rescan) in the same session (no biological variability) at different field strengths and vendors (Siemens, GE, Philips) in 3 centres to allow generalizability. The protocol consisted of long axis cines and a short axis (SAX) bSSFP cine stack. Between scans, patients were brought out of the bore, repositioned on the table and re-isocentered. Wall thickness : MWT was measured in the SAX cine stack in end-diastole (scans A and B) by 11 experts (from 4 continents, 6 countries, 9 centers). For ML performance, the contours were based on a repurposed algorithm used for brain cortical thickness measurement, applying the Laplace equation for all contour points – effectively creating nested smoothly deforming surfaces from endo- to epicardium. We created orthogonal field lines to connect endo-and epicardial points, measured these distances and took the maximum as MWT. Results: 1320 MWT measurements by experts were analyzed. Mean MWT varied significantly from 14.9 mm to 19.0 mm (Δ4.1 mm, p<0.05). MWT measured by ML fell in the middle of the experts (5 read higher, 4 lower, p<0.05). Experts had significantly different test:retest precision, ranging from 1.1±0.9 to 3.7±2.0 mm. ML precision performance surpassed all humans on all measures: precision 0.7±0.6 mm, p<0.05; Bland-Altman limits of agreement (ML 3.7 vs humans average 7.7 mm), and coefficient of variance (ML 4.3% vs experts 5.7–12.1%, p<0.05). Using ML, sudden cardiac death risk prediction would be 1.4 to 3.1 times more precise, and a clinical trial to detect a 2 mm MWT interval change would need 1.3 to 4 (mean 2.3) times fewer patients (beta=0.90, alfa=0.05). Conclusions: ML MWT measurement in HCM is superior to all international experts studied with implications for risk stratification and sample sizes for clinical trials.