[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121428-en":3,"doc-seo-121428-105":30,"detail-sidebar-cat-0-en-105":90},{"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":20,"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},121428,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Automated Detection of Shoulder Arthroplasty in X-Rays Using Machine Learning","Rising demand for shoulder arthroplasty outpaces hip and knee growth as the population remains both aging and active. Joint registries are crucial for monitoring long-term outcomes, detecting failure mechanisms, and guiding best clinical practice, yet current classification can be error-prone when encoded by non-medically trained personnel. This study evaluates machine learning to classify four shoulder arthroplasty technique categories from postoperative X-rays using a balanced dataset of 1,000 samples and 10-fold cross-validation across four neural network models.","Automated Detection of Shoulder Arthroplasty in X-rays Using  \nMachine Learning  \nAndrew Brunt*, Alistair Lawley*, Philip Riches, Jon Clarke, Philip Walmsley, Gordon Dobie  \nAbstract— Demand for shoulder arthroplasty is rising at a faster rate than hip and knee arthroplasty, driven by an increasingly aging yet active population. Joint registries are playing an increasingly critical role in tracking the long-term success of shoulder arthroplasty, identifying failure mechanisms, and shaping clinical best practices but current classification procedures are often performed by non-medically trained encoders leading to error. This study examines the use of machine learning in techniques to classify four broad categories of shoulder arthroplasty technique from postoperative x-rays. Data from the Scottish Arthroplasty Project, was used to create a balanced dataset of 1000 samples. A 10-fold cross validation was used for the training of 4 neural network models commonly used for classification of x-ray data. InceptionV3 model achieved the highest overall performance with an accuracy of 93.85% after cross validation, while EfficientNet demonstrated the highest individual classifier accuracy of 99% suggesting the potential to increase accuracy further in future studies.  \nClinical Relevance—This research highlights the potential of machine learning to enhance the accuracy of joint registry data encoding. Facilitating evidence-based improvements in implant design and surgical approaches through the use ofmore accurate data on implant survival, and revision rates.  \nI. INTRODUCTION  \nShoulder arthroplasty is increasingly used for the treatment of degenerative and traumatic conditions affecting the glenohumeral joint. The primary indications include osteoarthritis, rotator cuff arthropathy, inflammatory arthritis, and complex fractures. The goal of this procedure is to alleviate pain and restore joint function. The main types of shoulder arthroplasty include anatomic total shoulder arthroplasty (TSA), reverse shoulder arthroplasty (RSA), hemiarthroplasty, and resurfacing shoulder replacement. TSA replicates the native joint anatomy and is preferred in patients with an intact rotator cuff, whereas RSA is indicated for those with rotator cuff deficiency, exploiting altered shoulder biomechanics to enhance stability and function. Hemiarthroplasty, which involves replacing only the humeral head while preserving the native glenoid, was historically used for fractures and arthritis but has largely been replaced by RSA. Resurfacing arthroplasty is a bone-preserving technique where the prosthesis caps the humeral head. This is less commonly done but may be beneficial for younger patients with focal cartilage damage. The key difference between hemiarthroplasty and resurfacing arthroplasty is the  \n*These authors contributed equally to this work.  \nAndrew Brunt is with the NHS Golden Jubilee University National Hospital, Clydebank G81 4DY UK and the University of Strathclyde, 204 George St., Glasgow, G1 1XW, GB  \npresence of a stem in the former. While stemmed implants provide strong fixation, they can compromise bone stock, making future revisions more complex. This consideration is particularly important when selecting a procedure for younger patients, where preserving bone stock for potential future surgeries is important. [1, 2]  \nA. Increasing Demand and the Role of Joint Registries The use of shoulder arthroplasty is rising at a faster rate than hip and knee arthroplasty, driven by an increasingly aging yet active population, expanding indications, and technological advancements. Among these, RSA has experienced the most significant growth due to its effectiveness in treating conditions that were previously considered difficult to manage. [3] International joint registries have documented this trend, with a study from the United States reporting a substantial increase in the prevalence of shoulder arthroplasty over the past two decades. Since 19","cbCaiiNoj2SVQVxf","https://ap.wps.com/l/cbCaiiNoj2SVQVxf","pdf",1018803,1,4,"English","en",105,"# Introduction\n## Increasing Demand and the Role of Joint Registries\n## Machine Learning in Implant Identification","[{\"question\":\"Why is automated classification important for shoulder arthroplasty registries?\",\"answer\":\"Joint registries support long-term success tracking and best-practice development, but classification errors can arise from data entry and coding by non-medically trained encoders. Even small discrepancies can affect clinical decisions and research conclusions.\"},{\"question\":\"What dataset and validation approach were used in the machine learning study?\",\"answer\":\"The study used data from the Scottish Arthroplasty Project to build a balanced dataset of 1,000 samples. Training used a 10-fold cross-validation scheme across four neural network models.\"},{\"question\":\"Which model performed best overall and for individual classification accuracy?\",\"answer\":\"InceptionV3 achieved the highest overall performance with 93.85% accuracy after cross-validation. EfficientNet produced the highest individual classifier accuracy at 99%, suggesting room for further improvement in future work.\"}]","Automated Detection of Shoulder Arthroplasty in X-Rays Using Machine Learning | PDF",1785735621,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"automated-detection-of-shoulder-arthroplasty-in-x-rays-using-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/automated-detection-of-shoulder-arthroplasty-in-x-rays-using-machine-learning/121428/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is automated classification important for shoulder arthroplasty registries?","Question",{"text":74,"@type":75},"Joint registries support long-term success tracking and best-practice development, but classification errors can arise from data entry and coding by non-medically trained encoders. Even small discrepancies can affect clinical decisions and research conclusions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset and validation approach were used in the machine learning study?",{"text":79,"@type":75},"The study used data from the Scottish Arthroplasty Project to build a balanced dataset of 1,000 samples. Training used a 10-fold cross-validation scheme across four neural network models.",{"name":81,"@type":72,"acceptedAnswer":82},"Which model performed best overall and for individual classification accuracy?",{"text":83,"@type":75},"InceptionV3 achieved the highest overall performance with 93.85% accuracy after cross-validation. EfficientNet produced the highest individual classifier accuracy at 99%, suggesting room for further improvement in future work.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]