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A retrospective series of nine consecutive THA cases (March–September 2025) used a hybrid ensemble ML system with standardized radiographs and magnification correction, then validated predictions against actual intraoperative component selections via post-operative analysis. The system achieved 85.7% overall templating accuracy, with strong component-specific performance and no complications across follow-up.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-assisted-pre-operative-planning-for-total-hip-arthroplasty-accuracy-validation-and-post-operative-critical-analysis/449906/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-assisted-pre-operative-planning-for-total-hip-arthroplasty-accuracy-validation-and-post-operative-critical-analysis/449906.png","ImageObject",300,407,{"name":92,"@type":93},"pixelkiddo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was the ML system validated in this study?","Question",{"text":112,"@type":113},"Predicted pre-operative templates were validated against actual intraoperative component selections, followed by post-operative analysis. This prediction-verification methodology was used to support clinical utility and safety.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What overall templating accuracy did the ML system achieve?",{"text":117,"@type":113},"The ML system achieved 85.7% overall templating accuracy across the nine THA cases.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the reported clinical safety outcomes during follow-up?",{"text":121,"@type":113},"Clinical outcomes showed a 100% success rate with zero complications and no reoperations at a mean follow-up of 17.2 ± 8.1 weeks.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},449906,1790789713,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},962090883892,"https://ap-avatar.wpscdn.com/avatar/e00115db34f3e0f11b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1790152879550218658","Original Article  Journal of Orthopaedic Case Reports 2025 December:15( 12) :Page 329-335   \nMachine Learning Assisted Pre-operative planning for Total Hip Arthroplasty: Accuracy Validation and Post Operative Critical Analysis  \nARetrospective Study  \nVinit Kumar Singh1, Himanshu Bajpai1, Prashant Kumar Sharma1, Gaurav Gupta2  \nLearning Point of the Article:  \nMachine Learning-assisted preoperative planning for total hip arthroplasty demonstrates perfect clinical safety and substantially improved accuracy compared to traditional templating methods, establishing readiness for clinical decision-support integration .  \nAbstract  \nIntroduction: Machine learning (ML) applications in pre-operative templating for total hip arthroplasty (THA) require systematic validation through prediction-verification methodologies to establish clinical utility and safety.  \nMaterials and Methods: We conducted a retrospective case series of 9 consecutive THA cases between March and September 2025. A hybrid ensemble ML system performed pre-operative templating using standardized radiographs with magnification correction. Predictions were systematically validated against actual intraoperative component selections through post-operative analysis. Primary outcomes included component-specific accuracy with descriptive statistics. Secondary outcomesincluded clinical safetyand feasibilityassessment.  \nResults: The ML system achieved 85.7% overall templating accuracy across all cases. Component-specific accuracies were stem type 100%(9/9), stem sizing 88.9%(8/9), cup sizing66.7%(6/9), head sizing 54.4%(5/9), and materialselection 95.6%(9/9). High templating accuracy (≥97.7%) was achieved in 44.4% ofcases (4/9). The system demonstrated conversion risk prediction capability in one case. Clinical outcomes showed a 100% success rate with zero complications and noreoperations ata mean of 17.2 ± 8.1weeks of follow-up.  \nConclusion: ML-assisted pre-operative templating demonstrates feasibility forTHAplanningwith encouraging accuracy results and excellent clinical safety. These findings support the potential for largervalidation studies. The prediction-verification methodology provides a systematic framework forML validation in orthopedic surgery.  \nKeywords: Machine learning, artificial intelligence, total hip arthroplasty, pre-operative planning, templating accuracy, post-operative validation.  \nIntroduction  \nTotal hip arthroplasty (THA) represents one of the most successful orthopedic procedures,with over 450,000procedures performed annually in the United States [1]. Accurate preoperative templating remains crucial for optimal componentselection, surgical planning, and implant longevity [2,3 ] .  \nTraditional templating methods rely on surgeon experience with reported accuracy rates of 40–80% for component sizing [4,5]. Machine learning (ML) applications in orthopedic surgery have demonstrated potential for improving diagnostic accuracy and surgical outcomes [6,7]. Recent studies show promising results in automated radiographic analysis and implant sizing prediction  \n\n| \u003Cbr>Access this article online\u003Cbr>\u003Cbr>Website: [www.jocr.co.in](www.jocr.co.in)\u003Cbr>\u003Cbr>DOI:\u003Cbr>[https://doi.org/10.13107/jocr.2025.v15.i12.6550](https://doi.org/10.13107/jocr.2025.v15.i12.6550) | Author’s Photo Gallery |  |  |  |\n| --- | --- | --- | --- | --- |\n|  | | | | |\n|  | \u003Cbr>1Department of Orthopaedics, RAMA Medical College Hospital and Research Centre, RAMA University, Kanpur, Uttar Pradesh, India,²Department of Orthopaedics, Max Superspeciality Hospital, Dehradun, Uttarakhand, India.\u003Cbr>Address of Correspondence:\u003Cbr>Dr. Prashant Kumar Sharma,\u003Cbr>Rama Medical College Hospital and Research Centre, Rama University, Kanpur, Uttar Pradesh, India.\u003Cbr>[E-mail:](E-mail: drprashant.kg@gmail.com)[ drprashant.kg@gmail.com](E-mail: drprashant.kg@gmail.com) |  |  |  |\n| Submitted: 30/09/2025; Review: 10/10/2025; Accepted: November 2025; Published: December 2025 |  |  |  |  |\n| DOI: [https://doi.or","cbCaik3RZqrjRs1p","https://ap.wps.com/l/cbCaik3RZqrjRs1p","pdf",9428362,"English","# Introduction\n## Background and clinical need for accurate templating\n# Materials and Methods\n## Study design and ethics\n## Patient population and criteria\n## ML system and prediction-verification validation\n# Results\n## Overall and component-specific accuracy\n## Safety and clinical outcomes\n# Conclusion","[{\"question\":\"How was the ML system validated in this study?\",\"answer\":\"Predicted pre-operative templates were validated against actual intraoperative component selections, followed by post-operative analysis. This prediction-verification methodology was used to support clinical utility and safety.\"},{\"question\":\"What overall templating accuracy did the ML system achieve?\",\"answer\":\"The ML system achieved 85.7% overall templating accuracy across the nine THA cases.\"},{\"question\":\"What were the reported clinical safety outcomes during follow-up?\",\"answer\":\"Clinical outcomes showed a 100% success rate with zero complications and no reoperations at a mean follow-up of 17.2 ± 8.1 weeks.\"}]","Machine Learning Assisted Pre-operative planning for Total Hip Arthroplasty - Accuracy Validation and Post Operative Critical Analysis | PDF",1790731471,18]