Fig. 2: Comparison with existing works.

a Two main types of ultrasound robotic systems: rule-based and learning-based. As learning-based methods scale up with data and model size, they will show superior generalizability compared to rule-based methods, further possessing the potential to surpass human experts. b Our philosophy is to embrace the scaling law, involving the large-scale collection of expert data, training scalable neural networks, and future deployment in real clinical settings to establish a loop that enables continuous data and model scaling. c We compare our system with existing works across four critical aspects: system flexibility, data scalability, comprehensiveness of medical examinations, and clinically-oriented evaluation. ⭐ indicates task difficulty. The “Incomplete” label denotes scans covering only a partial carotid artery segment (between the internal/external and common carotid-subclavian junctions), rather than the full vessel. The abbreviation “CCA” stands for the common carotid artery, with its upper end marking the bifurcation into internal/external carotid arteries and its lower end indicating the junction with the subclavian artery. The quantitative error metric evaluates errors in at least one area: target segmentation or biometric measurement.