Towards expert-level autonomous carotid ultrasonography with large-scale learning-based robotic system.
Authors
Affiliations (11)
Affiliations (11)
- Department of Automation, Tsinghua University, Beijing, China.
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
- Air Force Medical Center, Beijing, China.
- LeadVision Ltd, Beijing, China.
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
- Beijing Academy of Artificial Intelligence, Beijing, China.
- Chinese PLA General Hospital, Beijing, China.
- Chinese PLA General Hospital, Beijing, China. [email protected].
- Department of Automation, Tsinghua University, Beijing, China. [email protected].
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China. [email protected].
- Beijing Academy of Artificial Intelligence, Beijing, China. [email protected].
Abstract
Carotid ultrasound requires skilled operators due to small vessel dimensions and high anatomical variability, exacerbating sonographer shortages and diagnostic inconsistencies. Prior automation attempts, including rule-based approaches with manual heuristics and reinforcement learning trained in simulated environments, demonstrate limited generalizability and fail to complete real-world clinical workflows. Here, we present UltraBot, a fully learning-based autonomous carotid ultrasound robot, achieving human-expert-level performance through four innovations: (1) A unified imitation learning framework for acquiring anatomical knowledge and scanning operational skills; (2) A large-scale expert demonstration dataset (247,000 samples, 100 × scale-up), enabling embodied foundation models with strong generalization; (3) A comprehensive scanning protocol ensuring full anatomical coverage for biometric measurement and plaque screening; (4) The clinical-oriented validation showing over 90% success rates, expert-level accuracy, up to 5.5 × higher reproducibility across diverse unseen populations. Overall, we show that large-scale deep learning offers a promising pathway toward autonomous, high-precision ultrasonography in clinical practice.