Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging
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-...
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| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2025.1527686/full |
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| author | Xin-Xin Lin Ming-De Li Si-Min Ruan Wei-Ping Ke Hao-Ruo Zhang Hui Huang Shao-Hong Wu Mei-Qing Cheng Wen-Juan Tong Hang-Tong Hu Dan-Ni He Rui-Fang Lu Ya-Dan Lin Ming Kuang Ming Kuang Ming-De Lu Ming-De Lu Li-Da Chen Qing-Hua Huang Qing-Hua Huang Wei Wang |
| author_facet | Xin-Xin Lin Ming-De Li Si-Min Ruan Wei-Ping Ke Hao-Ruo Zhang Hui Huang Shao-Hong Wu Mei-Qing Cheng Wen-Juan Tong Hang-Tong Hu Dan-Ni He Rui-Fang Lu Ya-Dan Lin Ming Kuang Ming Kuang Ming-De Lu Ming-De Lu Li-Da Chen Qing-Hua Huang Qing-Hua Huang Wei Wang |
| author_sort | Xin-Xin Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d148a819fb0843ef98d689d0ec5f00ef |
| institution | Kabale University |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-d148a819fb0843ef98d689d0ec5f00ef2025-02-05T05:17:49ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-02-011210.3389/frobt.2025.15276861527686Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imagingXin-Xin Lin0Ming-De Li1Si-Min Ruan2Wei-Ping Ke3Hao-Ruo Zhang4Hui Huang5Shao-Hong Wu6Mei-Qing Cheng7Wen-Juan Tong8Hang-Tong Hu9Dan-Ni He10Rui-Fang Lu11Ya-Dan Lin12Ming Kuang13Ming Kuang14Ming-De Lu15Ming-De Lu16Li-Da Chen17Qing-Hua Huang18Qing-Hua Huang19Wei Wang20Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaCollege of Electronic Information, Guangxi Minzu University, Nanning, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaCollege of Electronic Information, Guangxi Minzu University, Nanning, ChinaSchool of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, ChinaPurposeThis 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.https://www.frontiersin.org/articles/10.3389/frobt.2025.1527686/fullautonomous robotsultrasoundreproducibilityconsistencyAI |
| spellingShingle | Xin-Xin Lin Ming-De Li Si-Min Ruan Wei-Ping Ke Hao-Ruo Zhang Hui Huang Shao-Hong Wu Mei-Qing Cheng Wen-Juan Tong Hang-Tong Hu Dan-Ni He Rui-Fang Lu Ya-Dan Lin Ming Kuang Ming Kuang Ming-De Lu Ming-De Lu Li-Da Chen Qing-Hua Huang Qing-Hua Huang Wei Wang Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging Frontiers in Robotics and AI autonomous robots ultrasound reproducibility consistency AI |
| title | Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| title_full | Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| title_fullStr | Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| title_full_unstemmed | Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| title_short | Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| title_sort | autonomous robotic ultrasound scanning system a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging |
| topic | autonomous robots ultrasound reproducibility consistency AI |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2025.1527686/full |
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