Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method

Andrew Lin; Nipun Manral; Priscilla McElhinney; Aditya Killekar; Hidenari Matsumoto; Jacek Kwiecinski; Konrad Pieszko; Aryabod Razipour; Kajetan Grodecki; Caroline Park; Mhairi Doris; Alan Kwan; Donghee Han; Keiichiro Kuronama; Guadalupe Flores Tomasino; Evangelos Tzolos; Aakash Shanbhag; Markus Goeller; Mohamed Marwan; Sebastien Cadet; Stephan Achenbach; Stephen Nicholls; Dennis Wong; Daniel Berman; Marc Dweck; David Newby; Michelle E. Williams; Piotr Slomka; Damini Dey

doi:10.1117/12.2613244

4 April 2022 Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method

Andrew Lin, Nipun Manral, Priscilla McElhinney, Aditya Killekar, Hidenari Matsumoto, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Caroline Park, Mhairi Doris, Alan Kwan, Donghee Han, Keiichiro Kuronama, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Markus Goeller, Mohamed Marwan, Sebastien Cadet, Stephan Achenbach, Stephen Nicholls, Dennis Wong, Daniel Berman, Marc Dweck, David Newby, Michelle E. Williams, Piotr Slomka, Damini Dey

Author Affiliations +

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120312W (2022) https://doi.org/10.1117/12.2613244
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Background: Coronary computed tomography angiography (CCTA) allows non-invasive assessment of luminal stenosis and coronary atherosclerotic plaque. We aimed to develop and externally validate an artificial intelligence-based deep learning (DL) network for CCTA-based measures of plaque volume and stenosis severity. Methods: This was an international multicenter study of 1,183 patients undergoing CCTA at 11 sites. A novel DL convolutional neural network was trained to segment coronary plaque in 921 patients (5,045 lesions). The DL architecture consisted of a novel hierarchical convolutional long short-term memory (ConvLSTM) Network. The training set was further split temporally into training (80%) and internal validation (20%) datasets. Each coronary lesion was assessed in a 3D slab about the vessel centrelines. Following training and internal validation, the model was applied to an independent test set of 262 patients (1,469 lesions), which included an external validation cohort of 162 patients Results: In the test set, there was excellent agreement between DL and clinician expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.964) and percent diameter stenosis (ICC 0.879; both p<0.001, see tables and figure). The average per-patient DL plaque analysis time was 5.7 seconds versus 25-30 minutes taken by experts. There was significantly higher overlap measured by the Dice coefficient (DC) for ConvLSTM compared to UNet (DC for vessel 0.94 vs 0.83, p<0.0001; DC for lumen and plaque 0.90 vs 0.83, p<0.0001) or DeepLabv3 (DC for vessel both 0.94; DC for lumen and plaque 0.89 vs 0.84, p<0.0001). Conclusions: A novel externally validated artificial intelligence-based network provides rapid measurements of plaque volume and stenosis severity from CCTA which agree closely with clinician expert readers.

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Andrew Lin, Nipun Manral, Priscilla McElhinney, Aditya Killekar, Hidenari Matsumoto, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Caroline Park, Mhairi Doris, Alan Kwan, Donghee Han, Keiichiro Kuronama, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Markus Goeller, Mohamed Marwan, Sebastien Cadet, Stephan Achenbach, Stephen Nicholls, Dennis Wong, Daniel Berman, Marc Dweck, David Newby, Michelle E. Williams, Piotr Slomka, and Damini Dey "Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120312W (4 April 2022); https://doi.org/10.1117/12.2613244

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