[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122929-en":3,"doc-seo-122929-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122929,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning in Robotic Ultrasound Imaging - Challenges and Perspectives","The article reviews recent advances in intelligent robotic ultrasound imaging systems, starting with widely used robotic mechanisms and control techniques and their clinical applications. It then details how machine learning is used to develop robotic sonographers, organizing approaches for autonomous action reasoning into those based on implicit environmental data interpretation and those using explicit interpretation. Practical barriers are discussed, including limited medical datasets, the need for deeper understanding of ultrasound physics, and effective data representation, followed by open problems and prospective research directions for the community.","arXiv :2401 .02376v 1 [ cs .RO] 4 Jan 2024  \nMachine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives  \nYuan Bi*, Zhongliang Jiang*, Felix Duelmer, Dianye Huang, and Nassir Navab  \n* The first two authors contributed equally.  \nComputer Aided Medical Procedures, Technical University of Munich, Munich, Germany;  \nemail: [yuan.bi@tum.de](yuan.bi@tum.de), [zl.jiang@tum.de](zl.jiang@tum.de), [felix.duelmer@tum.de](felix.duelmer@tum.de),  \n[dianye.huang@tum.de](dianye.huang@tum.de), [nassir.navab@tum.de](nassir.navab@tum.de)  \nXxxx. Xxx. Xxx. Xxx. YYYY. AA:1–24 [https://doi.org/10.1146/](https://doi.org/10.1146/) ((please add article doi))  \nCopyright © YYYY by the author(s) . All rights reserved  \nKeywords  \nmachine learning, deep learning, segmentation, registration, robotic ultrasound, ultrasound image analysis, ultrasound simulation, data augmentation, reinforcement learning, learning from demonstration, ultrasound physics, ethics and regulations, medical robotics  \nAbstract  \nThis article reviews the recent advances in intelligent robotic ultrasound (US) imaging systems. We commence by presenting the commonly employed robotic mechanisms and control techniques in robotic US imaging, along with their clinical applications. Subsequently, we focus on the deployment of machine learning techniques in the development of robotic sonographers, emphasizing crucial developments aimed at enhancing the intelligence of these systems. The methods for achieving autonomous action reasoning are categorized into two sets of approaches: those relying on implicit environmental data interpretation and those using explicit interpretation. Throughout this exploration, we also discuss practical challenges, including those related to the scarcity of medical data, the need for a deeper understanding of the physical aspects involved, and effective data representation approaches. Moreover, we conclude by highlighting the open problems in the field and analyzing different possible perspectives on how the community could move forward in this research area.  \n1. INTRODUCTION  \nOver the past few decades, particularly in the last ten years, the advancement of autonomous medical robots has attracted increasing attention (1, 2) . An intelligent robotic colleague is envisioned to work with medical staffs in hospitals owing to the recent advances in fundamental sensing systems and artificial intelligence (3, 4, 5) . Among the various subfields of medical robotics, robotic sonography has particularly garnered attention from both the scientific and industrial sectors (6, 7, 8) . This surge in interest can be attributed to the fact that robotic ultrasound (US) examinations are generally non-invasive compared to other surgical robots, resulting in fewer ethical, legal, and regulatory concerns.  \nDue to the advantages of being portable, real-time, and ionizing radiation-free, medical US has gained widespread use in primary healthcare. The rapid evolution of medical imaging modalities throughout the last century has transformed imaging into an indispensable element of standard screening and diagnostic protocols. However, the development of interactive and dynamic imaging for treatment guidance and intervention has lagged behind. This can be attributed, in part, to the intricate patient and procedure-specific demands they entail. Moreover, their considerable reliance on user proficiency and usability considerations has also contributed to this gradual advancement (9) . To improve reproducibility and ensure consistent diagnosis, Salcudean et al. introduced the use of robotic systems to assist in US acquisition (10) . By accurately maneuvering the US probe, robotic US systems (RUSS) are expected to standardize examination protocols and optimize the imaging quality (11, 12) . In light of the increasing demand for healthcare interventions and the uneven distribution of experienced sonographers, there is a pressing need for the development of RUSS systems w","cbCail8WVgAx0mrB","https://ap.wps.com/l/cbCail8WVgAx0mrB","pdf",1934821,1,24,"English","en",105,"# Introduction\n## Motivation and clinical relevance\n## Robotic ultrasound systems and autonomy needs\n## Toward intelligent robotic sonographers\n## Machine learning approaches and representative methods","[{\"question\":\"Why is autonomy important for robotic ultrasound systems?\",\"answer\":\"It addresses the need for consistent examination quality and autonomy due to uneven distribution of experienced sonographers and the requirement to standardize US acquisition while ensuring patient safety.\"}]","Machine Learning in Robotic Ultrasound Imaging - Challenges and Perspectives | PDF",1785813727,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-in-robotic-ultrasound-imaging-challenges-and-perspectives","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-robotic-ultrasound-imaging-challenges-and-perspectives/122929/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Why is autonomy important for robotic ultrasound systems?","Question",{"text":75,"@type":76},"It addresses the need for consistent examination quality and autonomy due to uneven distribution of experienced sonographers and the requirement to standardize US acquisition while ensuring patient safety.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,101,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":29,"slug":100},5,"Comic","comic",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]