Literature DB >> 35232490

Artificial intelligence in orthopedic surgery: evolution, current state and future directions.

Andrew P Kurmis1,2, Jamie R Ianunzio3,4.   

Abstract

Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery.While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.
© 2022. The Author(s).

Entities:  

Keywords:  AI; Arthroplasty; Artificial intelligence; Machine learning

Year:  2022        PMID: 35232490      PMCID: PMC8889658          DOI: 10.1186/s42836-022-00112-z

Source DB:  PubMed          Journal:  Arthroplasty        ISSN: 2524-7948


  43 in total

1.  Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty.

Authors:  Akash A Shah; Sai K Devana; Changhee Lee; Reza Kianian; Mihaela van der Schaar; Nelson F SooHoo
Journal:  J Arthroplasty       Date:  2020-12-30       Impact factor: 4.757

2.  Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip.

Authors:  Jaret M Karnuta; Heather S Haeberle; Bryan C Luu; Alexander L Roth; Robert M Molloy; Lukas M Nystrom; Nicolas S Piuzzi; Jonathan L Schaffer; Antonia F Chen; Richard Iorio; Viktor E Krebs; Prem N Ramkumar
Journal:  J Arthroplasty       Date:  2020-11-16       Impact factor: 4.757

3.  Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.

Authors:  Yong-Hao Pua; Hakmook Kang; Julian Thumboo; Ross Allan Clark; Eleanor Shu-Xian Chew; Cheryl Lian-Li Poon; Hwei-Chi Chong; Seng-Jin Yeo
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2019-12-12       Impact factor: 4.342

4.  Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

Authors:  Prem N Ramkumar; Jaret M Karnuta; Sergio M Navarro; Heather S Haeberle; Giles R Scuderi; Michael A Mont; Viktor E Krebs; Brendan M Patterson
Journal:  J Arthroplasty       Date:  2019-06-20       Impact factor: 4.757

5.  Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

Authors:  Mark Alan Fontana; Stephen Lyman; Gourab K Sarker; Douglas E Padgett; Catherine H MacLean
Journal:  Clin Orthop Relat Res       Date:  2019-06       Impact factor: 4.176

6.  Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests.

Authors:  Riley A Bloomfield; Harley A Williams; Jordan S Broberg; Brent A Lanting; Kenneth A McIsaac; Matthew G Teeter
Journal:  J Arthroplasty       Date:  2019-06-05       Impact factor: 4.757

7.  Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

Authors:  Alex H S Harris; Alfred C Kuo; Thomas R Bowe; Luisa Manfredi; Narlina F Lalani; Nicholas J Giori
Journal:  J Arthroplasty       Date:  2020-07-20       Impact factor: 4.757

8.  Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery.

Authors:  Michael Murphy; Cameron Killen; Robert Burnham; Fahad Sarvari; Karen Wu; Nicholas Brown
Journal:  Hip Int       Date:  2021-01-08       Impact factor: 2.135

9.  Comparison of an Artificial Intelligence-Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis: A Randomized Clinical Trial.

Authors:  Prakash Jayakumar; Meredith G Moore; Kenneth A Furlough; Lauren M Uhler; John P Andrawis; Karl M Koenig; Nazan Aksan; Paul J Rathouz; Kevin J Bozic
Journal:  JAMA Netw Open       Date:  2021-02-01

Review 10.  A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations.

Authors:  Salih Beyaz
Journal:  Jt Dis Relat Surg       Date:  2020
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  1 in total

1.  Will technology drive orthopaedic surgery in the future?

Authors:  Raju Vaishya; Marius M Scarlat; Karthikeyan P Iyengar
Journal:  Int Orthop       Date:  2022-07       Impact factor: 3.479

  1 in total

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