AUTHOR=Lin Xin-Xin , Li Ming-De , Ruan Si-Min , Ke Wei-Ping , Zhang Hao-Ruo , Huang Hui , Wu Shao-Hong , Cheng Mei-Qing , Tong Wen-Juan , Hu Hang-Tong , He Dan-Ni , Lu Rui-Fang , Lin Ya-Dan , Kuang Ming , Lu Ming-De , Chen Li-Da , Huang Qing-Hua , Wang Wei 

TITLE=Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1527686

DOI=10.3389/frobt.2025.1527686

ISSN=2296-9144

ABSTRACT=PurposeThis study aims to develop an autonomous robotic ultrasound scanning system (auto-RUSS) pipeline, comparing its reproducibility and observer consistency in image analysis with physicians of varying levels of expertise.Design/methodology/approachAn auto-RUSS was engineered using a 7-degree-of-freedom robotic arm, with real-time regulation based on force control and ultrasound visual servoing. Two phantoms were employed for the human-machine comparative experiment, involving three groups: auto-RUSS, non-expert (4 junior physicians), and expert (4 senior physicians). This setup enabled comprehensive assessment of reproducibility in contact force, image acquisition, image measurement and AI-assisted classification. Radiological feature variability was measured using the coefficient of variation (COV), while performance and reproducibility assessments utilized mean and standard deviation (SD).FindingsThe auto-RUSS had the potential to reduce operator-dependent variability in ultrasound examinations, offering enhanced repeatability and consistency across multiple dimensions including probe contact force, images acquisition, image measurement, and diagnostic model performance.Originality/valueIn this paper, an autonomous robotic ultrasound scanning system (auto-RUSS) pipeline was proposed. Through comprehensive human-machine comparison experiments, the auto-RUSS was shown to effectively improve the reproducibility of ultrasound images and minimize human-induced variability.