Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort

Objective To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers
on low-dose chest CT.
Methods 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.
Results 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%).
Conclusion 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.