Literature DB >> 36104993

Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review.

Vishal Kumar1, Sandeep Patel1, Vishnu Baburaj1, Aditya Vardhan1, Prasoon Kumar Singh1, Raju Vaishya2.   

Abstract

Background: Artificial Intelligence (AI) has improved the way of looking at technological challenges. Today, we can afford to see many of the problems as just an input-output system rather than solving from the first principles. The field of Orthopaedics is not spared from this rapidly expanding technology. The recent surge in the use of AI can be attributed mainly to advancements in deep learning methodologies and computing resources. This review was conducted to draw an outline on the role of AI in orthopaedics.
Methods: We developed a search strategy and looked for articles on PubMed, Scopus, and EMBASE. A total of 40 articles were selected for this study, from tools for medical aid like imaging solutions, implant management, and robotic surgery to understanding scientific questions.
Results: A total of 40 studies have been included in this review. The role of AI in the various subspecialties such as arthroplasty, trauma, orthopaedic oncology, foot and ankle etc. have been discussed in detail.
Conclusion: AI has touched most of the aspects of Orthopaedics. The increase in technological literacy, data management plans, and hardware systems, amalgamated with the access to hand-held devices like mobiles, and electronic pads, augur well for the exciting times ahead in this field. We have discussed various technological breakthroughs in AI that have been able to perform in Orthopaedics, and also the limitations and the problem with the black-box approach of modern AI algorithms. We advocate for better interpretable algorithms which can help both the patients and surgeons alike.
© 2022 Professor P K Surendran Memorial Education Foundation. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Arthroplasty; Artificial intelligence; Deep learning; Machine learning; Orthopaedics; Trauma

Year:  2022        PMID: 36104993      PMCID: PMC9465367          DOI: 10.1016/j.jor.2022.08.020

Source DB:  PubMed          Journal:  J Orthop        ISSN: 0972-978X


  46 in total

1.  Deep learning approach for survival prediction for patients with synovial sarcoma.

Authors:  Ilkyu Han; June Hyuk Kim; Heeseol Park; Han-Soo Kim; Sung Wook Seo
Journal:  Tumour Biol       Date:  2018-09

2.  Semi-automated detection of anterior cruciate ligament injury from MRI.

Authors:  Ivan Štajduhar; Mihaela Mamula; Damir Miletić; Gözde Ünal
Journal:  Comput Methods Programs Biomed       Date:  2016-12-15       Impact factor: 5.428

3.  Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection.

Authors:  Christian Klemt; Samuel Laurencin; Akachimere Cosmas Uzosike; Jillian C Burns; Timothy G Costales; Ingwon Yeo; Yasamin Habibi; Young-Min Kwon
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2021-11-11       Impact factor: 4.114

4.  A primary care back pain screening tool: identifying patient subgroups for initial treatment.

Authors:  Jonathan C Hill; Kate M Dunn; Martyn Lewis; Ricky Mullis; Chris J Main; Nadine E Foster; Elaine M Hay
Journal:  Arthritis Rheum       Date:  2008-05-15

5.  Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning.

Authors:  Jun S Kim; Varun Arvind; Eric K Oermann; Deepak Kaji; Will Ranson; Chierika Ukogu; Awais K Hussain; John Caridi; Samuel K Cho
Journal:  Spine Deform       Date:  2018 Nov - Dec

6.  Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

Authors:  Oishila Bandyopadhyay; Arindam Biswas; Bhargab B Bhattacharya
Journal:  J Digit Imaging       Date:  2019-04       Impact factor: 4.056

7.  Evaluation of a Weightbearing CT Artificial Intelligence-Based Automatic Measurement for the M1-M2 Intermetatarsal Angle in Hallux Valgus.

Authors:  Jonathan Day; Cesar de Cesar Netto; Martinus Richter; Nacime Salomao Mansur; Celine Fernando; Jonathan T Deland; Scott J Ellis; François Lintz
Journal:  Foot Ankle Int       Date:  2021-06-04       Impact factor: 2.827

8.  The Analysis of Plantar Pressure Data Based on Multimodel Method in Patients with Anterior Cruciate Ligament Deficiency during Walking.

Authors:  Xiaoli Li; Hongshi Huang; Jie Wang; Yuanyuan Yu; Yingfang Ao
Journal:  Biomed Res Int       Date:  2016-12-06       Impact factor: 3.411

9.  Robot-assisted Percutaneous Pedicle Screw Placement Using Three-Dimensional Fluoroscopy: A Preliminary Clinical Study.

Authors:  Wei Tian; Ming-Xing Fan; Ya-Jun Liu
Journal:  Chin Med J (Engl)       Date:  2017-07-05       Impact factor: 2.628

10.  A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis.

Authors:  Yanping Xue; Rongguo Zhang; Yufeng Deng; Kuan Chen; Tao Jiang
Journal:  PLoS One       Date:  2017-06-02       Impact factor: 3.240

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