Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Autor:	Ha S; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea., Seo SY; Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea., Park BS; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea., Han S; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea., Oh JS; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea., Chae SY; Department of Nuclear Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Republic of Korea., Kim JS; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea., Moon DH; From the Department of Nuclear Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Jazyk:	angličtina
Zdroj:	Clinical nuclear medicine [Clin Nucl Med] 2024 Aug 01; Vol. 49 (8), pp. 727-732. Date of Electronic Publication: 2024 May 31.
DOI:	10.1097/RLU.0000000000005275
Abstrakt:	Purpose: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement. Patients and Methods: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model's performance was evaluated using Lin's concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement. Results: A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%. Conclusions: Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements. Competing Interests: Conflicts of interest and sources of funding: none declared. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT; no. NRF-2020M2D9A1094074; 2021R1A2C3009056) and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (HI18C2383). (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
Databáze:	MEDLINE
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