Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease

• Published in: Atherosclerosis 2021-05, Vol.324, p.69-75
• Main Authors: Cho, Hyungjoo, Kang, Soo-Jin, Min, Hyun-Seok, Lee, June-Goo, Kim, Won-Jang, Kang, Se Hun, Kang, Do-Yoon, Lee, Pil Hyung, Ahn, Jung-Min, Park, Duk-Woo, Lee, Seung-Whan, Kim, Young-Hak, Lee, Cheol Whan, Park, Seong-Wook, Park, Seung-Jung
• Format: Article
• Language: English
• Subjects: Attenuated plaque; Calcification; Deep learning; Intravascular ultrasound; Plaque characterization
• ISSN: 0021-9150; 1879-1484
• DOI: 10.1016/j.atherosclerosis.2021.03.037

Although plaque characterization by intravascular ultrasound (IVUS) is important for risk stratification, frame-by-frame analysis of a whole vascular segment is time-consuming. The aim was to develop IVUS-based algorithms for classifying attenuation and calcified plaques. IVUS image sets of 598 coronary arteries from 598 patients were randomized into training and test sets with 5:1 ratio. Each IVUS frame at a 0.4-mm interval was circumferentially labeled as one of three classes: attenuated plaque, calcified plaque, or plaque without attenuation or calcification. The model was trained on multi-class classification with 5-fold cross validation. By converting from Cartesian to polar coordinate images, the class corresponding to each array from 0 to 360° was plotted. At the angle-level, Dice similarity coefficients for identifying calcification vs. attenuation vs. none by using ensemble model were 0.79, 0.74 and 0.99, respectively. Also, the maximal accuracy was 98% to classify those groups in the test set. At the frame-level, the model identified the presence of attenuation with 80% sensitivity, 96% specificity, and 93% overall accuracy, and the presence of calcium with 86% sensitivity, 97% specificity, and 96% overall accuracy. In the per-vessel analysis, the attenuation and calcification burden index closely correlated with human measurements (r = 0.89 and r = 0.95, respectively), as did the maximal attenuation and calcification burden index over 4 mm (r = 0.82 and r = 0.91, respectively). The inference times were 0.05 s per frame and 7.8 s per vessel. Our deep learning algorithms for plaque characterization may assist clinicians in recognizing high-risk coronary lesions. [Display omitted] •Deep learning algorithms allow automatic learning without explicit programming, potentially improving diagnostic accuracy.•The IVUS-based model demonstrated good performance in classifying and quantifying tissue attenuation and calcification.•It supports clinicians in identifying high-risk lesions that potentially lead to stent under expansion and adverse events.