Youngtaek Hong1,2, Frederic Commandeur2, Sebastien Cadet3, Markus Goeller2,4, Mhairi K Doris3,5, Xi Chen3, Jacek Kwiecinski3,5, Daniel S Berman3, Piotr J Slomka3, Hyuk-Jae Chang6, Damini Dey2. 1. Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Republic of Korea. 2. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 3. Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 4. Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Medicine, Department of Cardiology, Erlangen, Germany. 5. Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom. 6. Department of internal medicine, Division of Cardiology, Severance Cardiovascular Hosp, Yonsei University College of Medicine, Seoul, Korea, Republic of.
Abstract
BACKGROUND: Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. METHODS: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. RESULTS: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. CONCLUSIONS: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.
BACKGROUND: Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. METHODS: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. RESULTS: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. CONCLUSIONS: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.
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