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Evidence from PRISMA-ScR-guided searches (Ovid MEDLINE and Embase, 1995–2025) shows strong technical performance, but limited external validation and no identified prospective clinical trials.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/computer-vision-in-lower-limb-orthopaedics-a-scoping-review-of-imaging-based-artificial-intelligence-applications/445104/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/computer-vision-in-lower-limb-orthopaedics-a-scoping-review-of-imaging-based-artificial-intelligence-applications/445104.png","ImageObject",300,407,{"name":42,"@type":43},"LangkahRina","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-04","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What does this scoping review evaluate in lower limb orthopaedics?","Question",{"text":62,"@type":63},"It systematically maps how computer vision is applied to lower limb orthopaedic imaging, identifying key tasks, imaging modalities, and algorithmic trends across published studies.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which study types and imaging regions were included or excluded?",{"text":67,"@type":63},"Included studies used automated or semiautomated computer vision methods on hip, knee, ankle, or foot imaging; excluded were conference abstracts, ongoing clinical trials, and studies without full text.",{"name":69,"@type":60,"acceptedAnswer":70},"What do the results suggest about model performance and validation?",{"text":71,"@type":63},"Reported diagnostic accuracy often exceeded 85% and segmentation Dice coefficients frequently exceeded 0.85, but only about 20% included external validation and no prospective clinical trials were identified.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},445104,1791124352,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":115,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":144},962090893776,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Review began 11/16/2025  \nReview ended 12/04/2025  \nPublished 12/07/2025  \n© Copyright 2025  \nAkram et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0. , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited.  \nDOI: 10.7759/cureus.98648  \nOpen Access Review Article  \nComputer Vision in Lower Limb Orthopaedics: AScoping Review of Imaging-Based Artificial Intelligence Applications  \nNimra Akram 1, Sirtaaj Mattoo 1 , Bilal Qaddoura 2, Donia Karimaghaei 3, Islam Hamarsheh 4, Sarkhell Radha 5  \n1. Trauma and Orthopaedics, Royal Berkshire NHS Foundation Trust, Reading, GBR 2. Trauma and Orthopaedics, University Hospital Southampton NHS Foundation Trust, Southampton, GBR 3. Trauma and Orthopaedics, Epsom and St Helier University Hospitals NHS Trust, London, GBR 4. Trauma and Orthopaedics, Croydon University Hospital, London, GBR 5. Orthopaedics, Croydon University Hospital, London, GBR  \nCorresponding author: Nimra Akram, [nimra.akram2@nhs.net](nimra.akram2@nhs.net)  \nAbstract  \nComputer vision and image-based artificial intelligence (AI) are increasingly being used in orthopaedic imaging. Applications in lower limb orthopaedic surgery present unique diagnostic, biomechanical and surgical planning challenges. This review systematically maps how computer vision has been applied to lower limb orthopaedic imaging, identifying key tasks, modalities and algorithmic trends in published studies.  \nA scoping review was conducted following Preferred Reporting Items for Systematic reviews and MetaAnalyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Ovid MEDLINE and Embase were searched from January 1995 to October 2025. Studies were included if they applied an automated or semiautomated computer vision method to imaging of the hip, knee, ankle or foot. Exclusions included conference abstracts, ongoing clinical trials and studies without full text.  \nTwenty studies met the inclusion criteria. The knee was the most frequently studied region (40%), followed by the hip (30%), ankle (15%) and foot (10%) . Radiographs (40%) and CT (45%) were the dominant imaging modalities, while MRI (10%) and ultrasound (5%) were less common. Deep learning was employed in 85% of studies, primarily using convolutional architectures such as U-Net, YOLO, and ResNet. Across tasks, reported diagnostic accuracies were typically above 85%, and segmentation Dice coefficients frequently exceeded 0 .85. Despite strong technical results, only 20% of studies included external validation, with no prospective clinical trials identified.  \nComputer vision research in lower limb orthopaedics is diversifying and progressing from diagnostic classification toward quantitative measurement and intraoperative integration. Despite promising accuracy and automation potential, the evidence base is constrained by predominantly single-centre retrospective designs and scarce external validation. Future studies should prioritise reproducibility, large-scale validation and clinical practice deployment.  \nCategories: Radiology, Trauma, Orthopedics  \nKeywords: computer vision, deep learning artificial intelligence, lower limb, msk radiology, orthopaedics trauma  \nIntroduction And Background  \nComputer vision and machine learning have become central to modern medical image analysis, providing automated classification and quantitative measurements with performance approaching that of expert clinicians [1,2] . In orthopaedics, these technologies offer opportunities to improve diagnostic consistency, reduce measurement variability and streamline surgical planning through three-dimensional reconstruction and modelling [3] . Interest has grown particularly in the lower limb, where imaging supports the assessment of trauma, arthroplasty planning and the evaluation of degenerative joint disease [4,5] .  \nThe clinical and economic burden of lower l","cbCailJ5AGDAtTZ8","https://ap.wps.com/l/cbCailJ5AGDAtTZ8","pdf",562339,"English","# Abstract\n# Introduction And Background\n## Clinical need in lower limb orthopaedics\n## Limitations of manual interpretation\n## Opportunities from computer vision","[{\"question\":\"What does this scoping review evaluate in lower limb orthopaedics?\",\"answer\":\"It systematically maps how computer vision is applied to lower limb orthopaedic imaging, identifying key tasks, imaging modalities, and algorithmic trends across published studies.\"},{\"question\":\"Which study types and imaging regions were included or excluded?\",\"answer\":\"Included studies used automated or semiautomated computer vision methods on hip, knee, ankle, or foot imaging; excluded were conference abstracts, ongoing clinical trials, and studies without full text.\"},{\"question\":\"What do the results suggest about model performance and validation?\",\"answer\":\"Reported diagnostic accuracy often exceeded 85% and segmentation Dice coefficients frequently exceeded 0.85, but only about 20% included external validation and no prospective clinical trials were identified.\"}]","Computer Vision in Lower Limb Orthopaedics - AScoping Review of Imaging-Based Artificial Intelligence Applications | PDF",1790710308,23]