Thomas J York, Bartosz Szyszka, Angela Brivio, Omar Musbahi, David Barrett, Justin P Cobb, Gareth G Jones
{"title":"用于识别适合进行部分膝关节置换术的候选者的放射学人工智能工具。","authors":"Thomas J York, Bartosz Szyszka, Angela Brivio, Omar Musbahi, David Barrett, Justin P Cobb, Gareth G Jones","doi":"10.1007/s00402-024-05589-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.</p><p><strong>Materials and methods: </strong>This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy.</p><p><strong>Results: </strong>1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).</p><p><strong>Conclusions: </strong>The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.</p>","PeriodicalId":8326,"journal":{"name":"Archives of Orthopaedic and Trauma Surgery","volume":" ","pages":"4963-4968"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582309/pdf/","citationCount":"0","resultStr":"{\"title\":\"A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.\",\"authors\":\"Thomas J York, Bartosz Szyszka, Angela Brivio, Omar Musbahi, David Barrett, Justin P Cobb, Gareth G Jones\",\"doi\":\"10.1007/s00402-024-05589-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.</p><p><strong>Materials and methods: </strong>This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy.</p><p><strong>Results: </strong>1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).</p><p><strong>Conclusions: </strong>The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.</p>\",\"PeriodicalId\":8326,\"journal\":{\"name\":\"Archives of Orthopaedic and Trauma Surgery\",\"volume\":\" \",\"pages\":\"4963-4968\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11582309/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Orthopaedic and Trauma Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00402-024-05589-8\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Orthopaedic and Trauma Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00402-024-05589-8","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/3 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.
Introduction: Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.
Materials and methods: This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy.
Results: 1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).
Conclusions: The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.
期刊介绍：
"Archives of Orthopaedic and Trauma Surgery" is a rich source of instruction and information for physicians in clinical practice and research in the extensive field of orthopaedics and traumatology. The journal publishes papers that deal with diseases and injuries of the musculoskeletal system from all fields and aspects of medicine. The journal is particularly interested in papers that satisfy the information needs of orthopaedic clinicians and practitioners. The journal places special emphasis on clinical relevance.
"Archives of Orthopaedic and Trauma Surgery" is the official journal of the German Speaking Arthroscopy Association (AGA).
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