Human vs. machine vs. core lab for the assessment of coronary atherosclerosis with lumen and vessel contour segmentation with intravascular ultrasound.

Autor: Bass RD; School of Medicine, Georgetown University, Washington, DC, USA., Garcia-Garcia HM; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA. hector.m.garciagarcia@medstar.net.; Division of Interventional Cardiology of MedStar Cardiovascular Research Network at MedStar Washington Hospital Center, 110 Irving Street, Suite 4B-1, Washington, DC, 20010, USA. hector.m.garciagarcia@medstar.net., Sanz-Sánchez J; Hospital Universitari i Politecnic La Fe, Valencia, Spain.; Centro de Investigación Biomedica en Red (CIBERCV), Madrid, Spain., Ziemer PGP; National Laboratory for Scientific Computing, Petrópolis, Brazil., Bulant CA; National Scientific and Technical Research Council, Tandil, Argentina., Kuku KK; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA., Kahsay YA; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA., Beyene S; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA., Melaku G; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA., Otsuka T; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland., Choi J; School of Medicine, Georgetown University, Washington, DC, USA., Fernández-Peregrina E; Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA., Erdogan E; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK., Gonzalo N; Interventional Cardiology, Hospital Universitario Clínico San Carlos, Madrid, Spain., Bourantas CV; Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK., Blanco PJ; National Laboratory for Scientific Computing, Petrópolis, Brazil., Räber L; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Jazyk: angličtina
Zdroj: The international journal of cardiovascular imaging [Int J Cardiovasc Imaging] 2022 Jul; Vol. 38 (7), pp. 1431-1439. Date of Electronic Publication: 2022 Apr 19.
DOI: 10.1007/s10554-022-02563-6
Abstrakt: A machine learning (ML) algorithm for automatic segmentation of intravascular ultrasound was previously validated. It has the potential to improve efficiency, accuracy and precision of coronary vessel segmentation compared to manual segmentation by interventional cardiology experts. The aim of this study is to compare the performance of human readers to the machine and against the readings from a Core Laboratory. This is a post-hoc, cross-sectional analysis of the IBIS-4 study. Forty frames were randomly selected and analyzed by 10 readers of varying expertise two separate times, 1 week apart. Their measurements of lumen, vessel, plaque areas, and plaque burden were performed in an offline software. Among humans, the intra-observer variability was not statistically significant. For the total 80 frames, inter-observer variability between human readers, the ML algorithm and Core Laboratory for lumen area, vessel area, plaque area and plaque burden were not statistically different. For lumen area, however, relative differences between the human readers and the Core Lab ranged from 0.26 to 12.61%. For vessel area, they ranged from 1.25 to 9.54%. Efficiency between the ML algorithm and the readers differed notably. Humans spent 47 min on average to complete the analyses, while the ML algorithm took on average less than 1 min. The overall lumen, vessel and plaque means analyzed by humans and the proposed ML algorithm are similar to those of the Core Lab. Machines, however, are more time efficient. It is warranted to consider use of the ML algorithm in clinical practice.
(© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)
Databáze: MEDLINE
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