Artificial intelligence and machine learning in knee arthroplasty

IF 1.6 4区医学 Q3 ORTHOPEDICS

Knee Pub Date : 2025-02-28 DOI:10.1016/j.knee.2025.02.014

Hugo C. Rodriguez , Brandon D. Rust , Martin W Roche , Ashim Gupta

{"title":"Artificial intelligence and machine learning in knee arthroplasty","authors":"Hugo C. Rodriguez , Brandon D. Rust , Martin W Roche , Ashim Gupta","doi":"10.1016/j.knee.2025.02.014","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee arthroplasty (KA). These technologies utilize algorithms for tasks such as predictive analytics and image recognition, improving preoperative planning, intraoperative navigation, and postoperative complication anticipation. This systematic review presents AI-driven tools’ clinical implications in total and unicompartmental KA, focusing on enhancing patient outcomes and operational efficiency.</div></div><div><h3>Methods</h3><div>A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language till March 2024. Inclusion criteria targeted adult human models without geographical restrictions, specifically related to total or unicompartmental KA.</div></div><div><h3>Results</h3><div>A total of 153 relevant studies were identified, covering various aspects of ML application for KA. Topics of studies included imaging modalities (<em>n</em> = 28), postoperative primary KA complications (<em>n</em> = 26), inpatient status (length of stay, readmissions, and cost) (<em>n</em> = 24), implant configuration (<em>n</em> = 14), revision (<em>n</em> = 12), patient-reported outcome measures (PROMs) (<em>n</em> = 11), function (<em>n</em> = 11), procedural communication (<em>n</em> = 8), total knee arthroplasty/unicompartmental knee arthroplasty prediction (<em>n</em> = 6), outpatient status (<em>n</em> = 4), perioperative efficiency (<em>n</em> = 4), patient satisfaction (<em>n</em> = 3), opioid usage (<em>n</em> = 3). A total of 66 ML models were described, with 48.7% of studies using multiple approaches.</div></div><div><h3>Conclusion</h3><div>This review assesses ML applications in knee arthroplasty, highlighting their potential to improve patient outcomes. While current algorithms and AI show promise, our findings suggest areas for enhancement in predictive performance before widespread clinical adoption.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"54 ","pages":"Pages 28-49"},"PeriodicalIF":1.6000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968016025000298","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ORTHOPEDICS","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee arthroplasty (KA). These technologies utilize algorithms for tasks such as predictive analytics and image recognition, improving preoperative planning, intraoperative navigation, and postoperative complication anticipation. This systematic review presents AI-driven tools’ clinical implications in total and unicompartmental KA, focusing on enhancing patient outcomes and operational efficiency.

Methods

A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language till March 2024. Inclusion criteria targeted adult human models without geographical restrictions, specifically related to total or unicompartmental KA.

Results

A total of 153 relevant studies were identified, covering various aspects of ML application for KA. Topics of studies included imaging modalities (n = 28), postoperative primary KA complications (n = 26), inpatient status (length of stay, readmissions, and cost) (n = 24), implant configuration (n = 14), revision (n = 12), patient-reported outcome measures (PROMs) (n = 11), function (n = 11), procedural communication (n = 8), total knee arthroplasty/unicompartmental knee arthroplasty prediction (n = 6), outpatient status (n = 4), perioperative efficiency (n = 4), patient satisfaction (n = 3), opioid usage (n = 3). A total of 66 ML models were described, with 48.7% of studies using multiple approaches.

Conclusion

This review assesses ML applications in knee arthroplasty, highlighting their potential to improve patient outcomes. While current algorithms and AI show promise, our findings suggest areas for enhancement in predictive performance before widespread clinical adoption.

查看原文本刊更多论文

人工智能和机器学习在膝关节置换术中的应用

人工智能（AI）及其子集机器学习（ML）对临床医学，特别是膝关节置换术（KA）产生了重大影响。这些技术利用算法来完成预测分析和图像识别等任务，改进术前计划、术中导航和术后并发症预测。本系统综述介绍了人工智能驱动的工具在整体和单室KA中的临床意义，重点是提高患者的治疗效果和操作效率。方法系统检索Cochrane Central Register of Controlled Trials、Embase、OVID Medline、PubMed和Web of Science等多个数据库，按照PRISMA指南检索截至2024年3月发表的英文研究。纳入标准针对成人人体模型，没有地理限制，特别是与总KA或单室KA相关。结果共鉴定出153项相关研究，涵盖了KA中ML应用的各个方面。研究的主题包括成像技术(n = 28),术后主要并发症KA (n = 26),住院病人状态(再次入院呆的长度,和成本)(n = 24),植入配置(n = 14),修订(n = 12), patient-reported结果措施(舞会)(n = 11),函数(n = 11),程序通信(n = 8),全膝关节置换术/ unicompartmental膝关节置换术预测(n = 6),门诊状态(n = 4),围手术期(n = 4)效率,病人满意度(n = 3),阿片类药物使用（n = 3）。共描述了66个ML模型，其中48.7%的研究使用了多种方法。结论：本综述评估了ML在膝关节置换术中的应用，强调了其改善患者预后的潜力。虽然目前的算法和人工智能显示出希望，但我们的研究结果表明，在广泛临床应用之前，预测性能有待提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Knee 医学-外科

CiteScore

3.80

自引率

5.30%

发文量

171

审稿时长

6 months

期刊介绍： The Knee is an international journal publishing studies on the clinical treatment and fundamental biomechanical characteristics of this joint. The aim of the journal is to provide a vehicle relevant to surgeons, biomedical engineers, imaging specialists, materials scientists, rehabilitation personnel and all those with an interest in the knee. The topics covered include, but are not limited to: • Anatomy, physiology, morphology and biochemistry; • Biomechanical studies; • Advances in the development of prosthetic, orthotic and augmentation devices; • Imaging and diagnostic techniques; • Pathology; • Trauma; • Surgery; • Rehabilitation.