Towards expert-level autonomous carotid ultrasonography with large-scale learning-based robotic system

Haojun Jiang, Andrew Zhao, Qian Yang, Xiangjie Yan, Teng Wang, Yulin Wang, Ning Jia, Jiangshan Wang, Guokun Wu, Yang Yue, Shaqi Luo, Huanqian Wang, Ling Ren, Siming Chen, Pan Liu, Guocai Yao, Wenming Yang, Shiji Song, Xiang Li, Kunlun He et al. (1 additional authors not shown)

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. Ultrasound examination significantly relies on manual operation, which has significant downsides. The authors present UltraBot, a carotid ultrasound robot capable of automated scanning, measurement, and plaque screening, and build an embodied foundation model using deep learning for intelligent, high-precision ultrasound.

External IDs:doi:10.1038/s41467-025-62865-w