医学
血管内超声
冠状动脉疾病
动脉粥样硬化
放射科
分割
计算机断层血管造影
超声波
血管造影
核医学
人工智能
内科学
计算机科学
作者
Murat Çap,Anantharaman Ramasamy,Ramya Parasa,İbrahım Halıl Tanboğa,Soe Maung,K. S. Morgan,Nathan Angelo Lecaros Yap,Mazen Abou Gamrah,Hessam Sokooti,Pieter Kitslaar,Johan H. C. Reiber,Jouke Dijkstra,Ryo Torii,James Moon,Anthony Mathur,Andreas Baumbach,Francesca Pugliese,Christos V. Bourantas
标识
DOI:10.1016/j.jcct.2023.12.007
摘要
Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard.Fifty-one participants (150 vessels) with chronic coronary syndrome who underwent CCTA and 3-vessel NIRS-IVUS were included. CCTA analysis was performed by an expert and an automated edge detection method and their estimations were compared to NIRS-IVUS at a segment-, lesion-, and frame-level.Segment-level analysis demonstrated a similar performance of the two CCTA analyses (conventional and automatic) with large biases and limits of agreement compared to NIRS-IVUS estimations for the total atheroma (ICC: 0.55 vs 0.25, mean difference:192 (-102-487) vs 243 (-132-617) and percent atheroma volume (ICC: 0.30 vs 0.12, mean difference: 12.8 (-5.91-31.6) vs 20.0 (0.79-39.2). Lesion-level analysis showed that the experts were able to detect more accurately lesions than the automated method (68.2 % and 60.7 %) however both analyses had poor reliability in assessing the minimal lumen area (ICC 0.44 vs 0.36) and the maximum plaque burden (ICC 0.33 vs 0.33) when NIRS-IVUS was used as the reference standard.Conventional and automated CCTA analyses had similar performance in assessing coronary artery pathology using NIRS-IVUS as a reference standard. Therefore, automated segmentation can be used to expedite CCTA analysis and enhance its applications in clinical practice.
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