A Survey of Autonomous Robotic Ultrasound Scanning Systems
This review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems. Traditional ultrasound imaging depends on manual probe manipulation, which introduces operator variability, physical strain, and limitations in accessibility. To address these ch...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11016698/ |
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| author | Khushboo Munir Abdullah F. Al-Battal Ammar Alsheghri Harald Becher Michelle Noga Kumaradevan Punithakumar |
| author_facet | Khushboo Munir Abdullah F. Al-Battal Ammar Alsheghri Harald Becher Michelle Noga Kumaradevan Punithakumar |
| author_sort | Khushboo Munir |
| collection | DOAJ |
| description | This review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems. Traditional ultrasound imaging depends on manual probe manipulation, which introduces operator variability, physical strain, and limitations in accessibility. To address these challenges, this review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems by analyzing over 60 publications, including key developments from 2022 to 2025. Our survey reveals a growing adoption of cobot-based solutions equipped with 6-DOF force/torque sensors and RGB-D vision systems for precise probe positioning. Notably, several systems now integrate reinforcement learning, image-guided visual servoing, and real-time feedback loops to enable intelligent trajectory planning and adaptive force control. However, we identify critical gaps in the literature: surface-parallel force and torque components are often ignored in control models, limiting the accuracy of probe orientation and tissue coupling. Furthermore, real-time ultrasound image feedback is rarely used for path optimization, despite its importance in enhancing image quality and diagnostic reliability. This review emphasizes the need for future systems to integrate multi-modal sensing, adaptive control, and real-time image quality assessment to achieve robust, generalizable robotic ultrasound workflows. |
| format | Article |
| id | doaj-art-c3a0255069d84da1a3399a5ff01c094f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c3a0255069d84da1a3399a5ff01c094f2025-08-20T03:21:40ZengIEEEIEEE Access2169-35362025-01-011310317810319710.1109/ACCESS.2025.357446411016698A Survey of Autonomous Robotic Ultrasound Scanning SystemsKhushboo Munir0Abdullah F. Al-Battal1Ammar Alsheghri2Harald Becher3https://orcid.org/0000-0001-8770-8594Michelle Noga4https://orcid.org/0000-0001-5127-7374Kumaradevan Punithakumar5https://orcid.org/0000-0003-3835-1079Interdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaInterdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaInterdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaMazankowski Alberta Heart Institute, Edmonton, AB, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, CanadaThis review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems. Traditional ultrasound imaging depends on manual probe manipulation, which introduces operator variability, physical strain, and limitations in accessibility. To address these challenges, this review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems by analyzing over 60 publications, including key developments from 2022 to 2025. Our survey reveals a growing adoption of cobot-based solutions equipped with 6-DOF force/torque sensors and RGB-D vision systems for precise probe positioning. Notably, several systems now integrate reinforcement learning, image-guided visual servoing, and real-time feedback loops to enable intelligent trajectory planning and adaptive force control. However, we identify critical gaps in the literature: surface-parallel force and torque components are often ignored in control models, limiting the accuracy of probe orientation and tissue coupling. Furthermore, real-time ultrasound image feedback is rarely used for path optimization, despite its importance in enhancing image quality and diagnostic reliability. This review emphasizes the need for future systems to integrate multi-modal sensing, adaptive control, and real-time image quality assessment to achieve robust, generalizable robotic ultrasound workflows.https://ieeexplore.ieee.org/document/11016698/Computer-aided systemsechocardiographydeep learningmedical roboticsneural networksrobotic system and software |
| spellingShingle | Khushboo Munir Abdullah F. Al-Battal Ammar Alsheghri Harald Becher Michelle Noga Kumaradevan Punithakumar A Survey of Autonomous Robotic Ultrasound Scanning Systems IEEE Access Computer-aided systems echocardiography deep learning medical robotics neural networks robotic system and software |
| title | A Survey of Autonomous Robotic Ultrasound Scanning Systems |
| title_full | A Survey of Autonomous Robotic Ultrasound Scanning Systems |
| title_fullStr | A Survey of Autonomous Robotic Ultrasound Scanning Systems |
| title_full_unstemmed | A Survey of Autonomous Robotic Ultrasound Scanning Systems |
| title_short | A Survey of Autonomous Robotic Ultrasound Scanning Systems |
| title_sort | survey of autonomous robotic ultrasound scanning systems |
| topic | Computer-aided systems echocardiography deep learning medical robotics neural networks robotic system and software |
| url | https://ieeexplore.ieee.org/document/11016698/ |
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