Hamelink Iris, van Tuinen Marcel, Kwee Thomas C, van Ooijen Peter M A, Vliegenthart Rozemarijn
Department of Radiology, University of Groningen, University Medical Center of Groningen, Groningen, The Netherlands.
Department of Radiation Oncology, University of Groningen, University Medical Center of Groningen, Groningen, The Netherlands.
Eur Radiol. 2025 Jan 8. doi: 10.1007/s00330-024-11328-9.
To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.
We included participants of the population-based Imaging in Lifelines (ImaLife) study with low-dose chest CT at baseline and 3-4 month follow-up. An AI system (AI-Rad Companion chest CT prototype) performed automatic segmentation and quantification of vertebral height and density, aortic diameters, heart volume (cardiac chambers plus pericardial fat), and coronary artery calcium volume (CACV). A trained researcher visually checked segmentation accuracy. We evaluated the repeatability of adequate AI-based measurements at baseline and repeat scan using Intraclass Correlation Coefficient (ICC), relative differences, and change in CACV risk categorization, assuming no physiological change.
Overall, 632 participants (63 ± 11 years; 56.6% men) underwent short-term repeat CT (mean interval, 3.9 ± 1.8 months). Visual assessment showed adequate segmentation in both baseline and repeat scan for 98.7% of vertebral measurements, 80.1-99.4% of aortic measurements (except for the sinotubular junction (65.2%)), and 86.0% of CACV. For heart volume, 53.5% of segmentations were adequate at baseline and repeat scans. ICC for adequately segmented cases showed excellent agreement for all biomarkers (ICC > 0.9). Relative difference between baseline and repeat measurements was < 4% for vertebral and aortic measurements, 7.5% for heart volume, and 28.5% for CACV. There was high concordance in CACV risk categorization (81.2%).
In low-dose chest CT, segmentation accuracy of AI-based software was high for vertebral, aortic, and CACV evaluation and relatively low for heart volume. There was excellent repeatability of vertebral and aortic measurements and high concordance in overall CACV risk categorization.
Question Can AI algorithms for opportunistic screening in chest CT obtain an accurate and repeatable result when applied to multiple CT scans of the same participant? Findings Vertebral and aortic analysis showed accurate segmentation and excellent repeatability; coronary calcium segmentation was generally accurate but showed modest repeatability due to a non-electrocardiogram-triggered protocol. Clinical relevance Opportunistic screening for diseases outside the primary purpose of the CT scan is time-consuming. AI allows automated vertebral, aortic, and coronary artery calcium (CAC) assessment, with highly repeatable outcomes of vertebral and aortic biomarkers and high concordance in overall CAC categorization.
评估基于人工智能的低剂量胸部CT椎体和心血管标志物自动测量的可重复性。
我们纳入了基于人群的生命线成像（ImaLife）研究的参与者，这些参与者在基线和3 - 4个月随访时进行了低剂量胸部CT检查。一个人工智能系统（AI-Rad Companion胸部CT原型）对椎体高度和密度、主动脉直径、心脏容积（心腔加心包脂肪）以及冠状动脉钙化容积（CACV）进行自动分割和定量分析。一名经过培训的研究人员对分割准确性进行视觉检查。我们使用组内相关系数（ICC）、相对差异以及CACV风险分类的变化来评估基线和重复扫描时基于人工智能的充分测量的可重复性，假设没有生理变化。
总体而言，632名参与者（63±11岁；56.6%为男性）接受了短期重复CT检查（平均间隔时间为3.9±1.8个月）。视觉评估显示，在基线和重复扫描中，98.7%的椎体测量、80.1% - 99.4%的主动脉测量（除了窦管交界部（65.2%））以及86.0%的CACV分割是充分的。对于心脏容积，在基线和重复扫描时，53.5%的分割是充分的。对于分割充分的病例，所有生物标志物的ICC显示出极好的一致性（ICC>0.9）。椎体和主动脉测量的基线与重复测量之间的相对差异<4%，心脏容积为7.5%，CACV为28.5%。CACV风险分类具有高度一致性（81.2%）。
在低剂量胸部CT中，基于人工智能的软件在椎体、主动脉和CACV评估方面的分割准确性较高，而在心脏容积评估方面相对较低。椎体和主动脉测量具有出色的可重复性，并且在总体CACV风险分类方面具有高度一致性。
问题 在胸部CT的机会性筛查中，应用于同一参与者的多次CT扫描时，人工智能算法能否获得准确且可重复的结果？ 发现 椎体和主动脉分析显示分割准确且可重复性极佳；冠状动脉钙化分割总体准确，但由于采用非心电图触发方案，可重复性一般。 临床意义 对CT扫描主要目的之外的疾病进行机会性筛查很耗时。人工智能可实现椎体、主动脉和冠状动脉钙化（CAC）的自动评估，椎体和主动脉生物标志物的结果具有高度可重复性，并且在总体CAC分类方面具有高度一致性。
