[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127121-en":3,"doc-seo-127121-105":30,"detail-sidebar-cat-0-en-105":91},{"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},127121,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Systematic review of machine learning applications using nonoptical motion tracking in surgery","Systematic review of machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS). Reviews identify objectives, experimental designs, model effectiveness, and future research directions. Among 3632 records, 84 studies were included, highlighting common models such as artificial neural networks and support vector machines, and detailing typical NOMTS sensor types and surgical settings. Results indicate that NOMTS combined with ML can improve surgical precision, assessment, and training. Recommendations emphasize interpretability, reproducibility, and larger datasets for reliable evaluation.","EUR Research Information Portal  \nSystematic review of machine learning applications using nonoptical motion tracking in surgery  \nPublished in:  \nnpj Digital Medicine  \nPublication status and date:  \nPublished: 14/01/2025  \nDOI (link to publisher):  \n10.1038/s41746-024-01412-1  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nCarciumaru, T. Z. , Tang, C. M. , Farsi, M. , Bramer, W. M. , Dankelman, J. , Raman, C. , Dirven, C. M. F. , Gholinejad, M. , & Vasilic, D. (2025) . Systematic review of machine learning applications using nonoptical motion tracking in surgery. npj Digital Medicine, 8(1), Article 28. [https://doi.org/10.1038/s41746-024-01412-1](https://doi.org/10.1038/s41746-024-01412-1)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nnpj | digital medicine Article  \nPublished in partnership with Seoul National University Bundang Hospital  \n[https://doi.org/10.1038/s41746-024-01412-1](https://doi.org/10.1038/s41746-024-01412-1)  \nSystematic review of machine learning applications using nonoptical motion tracking in surgery  \n Check for updates  \nas poor quality and susceptibility to  \n-  \nidentifying NOMTS applications in surgical settings, and highlighting  \nndergoing screening based on title and  \nTeona Z. Carciumaru 1,2 , Cadey M. Tang 1, Mohsen Farsi1, Wichor M. Bramer3, Jenny Dankelman4, Chirag Raman 5, Clemens M. F. Dirven2, Maryam Gholinejad1,4 & Dalibor Vasilic1  \nThis systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records,84studies were included, withArtificial Neural Networks (38%) and Support Vector Machines (11%)beingthe most common ML models. Skill assessment was the primary objective(38%) . NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%) . Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%) . Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTSand ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation.  \nMachine learning (ML) models have gained consistent attention within the medical field for their potential to revolutionise healthcare practices. ML algorithms are adept at modellin","cbCaiujm4vzRNHZS","https://ap.wps.com/l/cbCaiujm4vzRNHZS","pdf",1218336,1,21,"English","en",105,"# Introduction\n## Machine learning in healthcare\n## Surgical motion tracking and limitations of optical sensors\n## Rationale for NOMTS\n# Review Scope and Objectives\n## Goals of the systematic review\n## Identification of ML algorithms and models","[{\"question\":\"What is the main purpose of the systematic review?\",\"answer\":\"To provide an overview of machine learning applications for surgical manoeuvre analysis using non-optical motion tracking systems (NOMTS), including objectives, study designs, and model effectiveness.\"},{\"question\":\"How many studies were included and what models were most common?\",\"answer\":\"From 3632 records, 84 studies were included. Artificial neural networks and support vector machines were among the most common ML models.\"},{\"question\":\"What types of non-optical motion tracking sensors and surgical settings are covered?\",\"answer\":\"NOMTS included internal device kinematics, electromagnetic, inertial, mechanical, and electromyography sensors. Surgical settings covered robotic, laparoscopic, open, and other contexts.\"}]","Systematic review of machine learning applications using nonoptical motion tracking in surgery | PDF",1785936951,53,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"systematic-review-of-machine-learning-applications-using-nonoptical-motion-tracking-in-surgery","",{"@graph":36,"@context":85},[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/systematic-review-of-machine-learning-applications-using-nonoptical-motion-tracking-in-surgery/127121/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of the systematic review?","Question",{"text":75,"@type":76},"To provide an overview of machine learning applications for surgical manoeuvre analysis using non-optical motion tracking systems (NOMTS), including objectives, study designs, and model effectiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many studies were included and what models were most common?",{"text":80,"@type":76},"From 3632 records, 84 studies were included. Artificial neural networks and support vector machines were among the most common ML models.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of non-optical motion tracking sensors and surgical settings are covered?",{"text":84,"@type":76},"NOMTS included internal device kinematics, electromagnetic, inertial, mechanical, and electromyography sensors. 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