Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review

被引:95
作者
Haeberle, Heather S. [1 ]
Helm, James M. [2 ]
Navarro, Sergio M. [1 ,3 ]
Karnuta, Jaret M. [2 ]
Schaffer, Jonathan L. [2 ]
Callaghan, John J. [4 ]
Mont, Michael A. [5 ]
Kamath, Atul F. [2 ]
Krebs, Viktor E. [2 ]
Ramkumar, Prem N. [2 ]
机构
[1] Baylor Coll Med, Dept Orthopaed Surg, Houston, TX 77030 USA
[2] Cleveland Clin, Machine Learning Arthroplasty Lab, Dept Orthoped Surg, 2049 E 100th St, Cleveland, OH 44195 USA
[3] Univ Oxford, Said Business Sch, Oxford, England
[4] Univ Iowa, Dept Orthopaed Surg, Iowa City, IA USA
[5] Dept Orthopaed Surg, Lenox Hill, New York, NY USA
关键词
machine learning; arthroplasty; value; big data; remote monitoring; DEEP; CLASSIFICATION;
D O I
10.1016/j.arth.2019.05.055
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
Background: Driven by the rapid development of big data and processing power, artificial intelligence and machine learning (ML) applications are poised to expand orthopedic surgery frontiers. Lower extremity arthroplasty is uniquely positioned to most dramatically benefit from ML applications given its central role in alternative payment models and the value equation. Methods: In this report, we discuss the origins and model specifics behind machine learning, consider its progression into healthcare, and present some of its most recent advances and applications in arthroplasty. Results: A narrative review of artificial intelligence and ML developments is summarized with specific applications to lower extremity arthroplasty, with specific lessons learned from osteoarthritis gait models, joint-specific imaging analysis, and value-based payment models. Conclusion: The advancement and employment of ML provides an opportunity to provide data-driven, high performance medicine that can rapidly improve the science, economics, and delivery of lower extremity arthroplasty. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:2201 / 2203
页数:3
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