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A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis

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Title: A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis
Authors: Bhuva, AN
Bai, W
Lau, C
Davies, RH
Ye, Y
Bulluck, H
McAlindon, E
Culotta, V
Swoboda, PP
Captur, G
Treibel, TA
Augusto, JB
Knott, KD
Seraphim, A
Cole, GD
Petersen, SE
Edwards, NC
Greenwood, JP
Bucciarelli-Ducci, C
Hughes, AD
Rueckert, D
Moon, JC
Manisty, CH
Item Type: Journal Article
Abstract: Background: Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set. Methods: One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models. Results: Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%–7.1%], P=0.2581; 8.3 [5.6%–10.3%], P=0.3653; 8.8 [6.1%–11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes). Conclusions: Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at www.thevolumesresource.com) permits a generalizable assessment of ML precision and may facilitate direct translation of ML to clinical practice.
Issue Date: 1-Oct-2019
Date of Acceptance: 25-Jul-2019
URI: http://hdl.handle.net/10044/1/77165
DOI: 10.1161/CIRCIMAGING.119.009214
ISSN: 1941-9651
Publisher: American Heart Association
Start Page: 1
End Page: 11
Journal / Book Title: Circulation: Cardiovascular Imaging
Volume: 12
Issue: 10
Copyright Statement: © 2019 The Authors. Circulation: Cardiovascular Imaging is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P001009/1
EP/R005982/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Cardiac & Cardiovascular Systems
Radiology, Nuclear Medicine & Medical Imaging
Cardiovascular System & Cardiology
artificial intelligence
image processing
left ventricular remodeling
magnetic resonance imaging cine
ventricular function
CARDIOVASCULAR MAGNETIC-RESONANCE
HEART-FAILURE
CARDIAC MR
VARIABILITY
QUANTIFICATION
SEGMENTATION
CONSENSUS
MASS
artificial intelligence
image processing
left ventricular remodeling
magnetic resonance imaging, cine
ventricular function
Science & Technology
Life Sciences & Biomedicine
Cardiac & Cardiovascular Systems
Radiology, Nuclear Medicine & Medical Imaging
Cardiovascular System & Cardiology
artificial intelligence
image processing
left ventricular remodeling
magnetic resonance imaging cine
ventricular function
CARDIOVASCULAR MAGNETIC-RESONANCE
HEART-FAILURE
CARDIAC MR
VARIABILITY
QUANTIFICATION
SEGMENTATION
CONSENSUS
MASS
Cardiovascular System & Hematology
1103 Clinical Sciences
Publication Status: Published
Open Access location: https://www.ahajournals.org/doi/pdf/10.1161/CIRCIMAGING.119.009214
Article Number: ARTN e009214
Online Publication Date: 2019-09-24
Appears in Collections:Computing
Department of Brain Sciences