In:
Circulation: Cardiovascular Imaging, Ovid Technologies (Wolters Kluwer Health), Vol. 12, No. 10 ( 2019-10)
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1941-9651
,
1942-0080
DOI:
10.1161/CIRCIMAGING.119.009214
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2019
detail.hit.zdb_id:
2440475-5
