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Ghadirinejad K, Milimonfared R, Taylor M, Solomon LB, Graves S, Pratt N, de Steiger R, Hashemi R. Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review. ANZ J Surg 2024. [PMID: 38597170 DOI: 10.1111/ans.19003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024]
Abstract
Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.
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Affiliation(s)
- Khashayar Ghadirinejad
- The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia
| | - Roohollah Milimonfared
- The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia
| | - Mark Taylor
- The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia
| | - Lucian B Solomon
- Department of Orthopaedics and Trauma, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Centre for Orthopaedic & Trauma Research, University of Adelaide, Adelaide, South Australia, Australia
| | - Stephen Graves
- Department of Surgery, Epworth HealthCare, The University of Melbourne, Parkville, Victoria, Australia
| | - Nicole Pratt
- The Australian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Richard de Steiger
- Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia
- Department of Surgery, Epworth HealthCare, The University of Melbourne, Parkville, Victoria, Australia
| | - Reza Hashemi
- The Medical Device Research Institute, College of Science and Engineering, Flinders University, Clovelly Park, South Australia, Australia
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Milimonfared R, Oskouei RH, Taylor M, Solomon LB. An intelligent system for image-based rating of corrosion severity at stem taper of retrieved hip replacement implants. Med Eng Phys 2018; 61:13-24. [DOI: 10.1016/j.medengphy.2018.08.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 07/27/2018] [Accepted: 08/05/2018] [Indexed: 11/29/2022]
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