Abstract Purpose Blood clot volume (BCV), defined as the total three-dimensional (3D) volume of the thrombus on computed tomography angiography (CTA), is an objective biomarker of pulmonary embolism (PE) severity whose clinical use is limited by time-consuming manual segmentation. This study evaluates ClotIA (Clot Interventional AI), a foundation model (FM)-based approach designed for rapid and interactive clot segmentation in PE. Materials and Methods RAPSv2, a foundation model derived from SAM2, was fine-tuned on a stratified sample of 309 patients from the RSPECT dataset (2020). Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and compared to that of nnUNet (no-new-Net). The predicted BCV was correlated with imaging biomarkers of PE severity. Results ClotIA achieved a mean DSC of 0.83 ± 0.06 after guided refinement, compared to 0.79 ± 0.10 at baseline ( p < 0.001) and 0.81 ± 0.13 for nnUNet ( p < 0.001). The predicted BCV showed strong agreement with the reference volume (r = 0.995; mean bias + 0.12 mL) and was significantly correlated with RV/LV diameter ratio (r = 0.62, p < 0.001) and RV/LV volume ratio (r = 0.68, p < 0.001). Conclusion ClotIA enables rapid and reproducible 3D quantification of pulmonary embolism thrombi, correlating with established severity markers and providing the necessary basis for translating emerging biomarkers into clinical practice.
Floch et al. (2026) studied this question.