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18. Swelling assessment after total knee arthroplasty

Author

LI Ka Yau, FU Henry, CHEUNG Man Hong, CHEUNG Amy, CHAN Wai Kwan Vincent, CHAN Ping Keung, and CHIU Kwong Yuen

Subjects
Orthopedic surgery, RD701-811
Abstract

Background Total knee arthroplasty is a commonly performed elective orthopaedic surgery. Patients may endure substantial knee swelling following surgery, which are attributable to both effusion and edema. Studies have been aiming to identify an accurate and reliable method to quantify post-operative knee swelling to aid monitoring progress and treatment. The aim of this article was to review the means of clinically applicable measurements for knee swelling post TKA. Methods The medical literature was searched using PubMed to search for articles published using the terms knee edema, effusion, swelling, knee arthroplasty, knee replacement, total knee arthroplasty, total knee replacement, TKA, TKR. Year of publication was not restricted. Only English language publications were included. Only full-text published articles from peer-reviewed journals were eligible for inclusion. The knee swelling measurement methods used in post TKA were reviewed. Results Advancement in bioimpedance spectroscopy and handheld 3D scanning technology allows quick and precise quantification of knee swelling volume that the traditional clinical circumferential measurement and volumetric measurement lack. Handheld 3D scanning is also a potential tool to estimate the change of knee effusion volume and muscular volume after the surgery. Magnetic resonance imaging is accurate in effusion measurement but also the most time and resource demanding method. Conclusion Bioimpedance spectroscopy and 3D scanning technology can be the future tools for clinically measurement of knee swelling after total knee arthroplasty.

Published
2022
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34. Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review.

Author

Chong, Yuk Yee, Chan, Ping Keung, Chan, Vincent Wai Kwan, Cheung, Amy, Luk, Michelle Hilda, Cheung, Man Hong, Fu, Henry, and Chiu, Kwong Yuen

Subjects
RISK of prosthesis complications, INFECTION prevention, ANTIBIOTICS, INFECTION risk factors, ONLINE information services, DEEP learning, KNEE osteoarthritis, ENTEROCOCCAL infections, SUPPORT vector machines, TOTAL knee replacement, SYSTEMATIC reviews, MACHINE learning, ARTIFICIAL intelligence, METHICILLIN-resistant staphylococcus aureus, RANDOM forest algorithms, ARTIFICIAL joints, RISK assessment, STAPHYLOCOCCAL diseases, KLEBSIELLA infections, SURGICAL site infections, ESCHERICHIA coli diseases, PSEUDOMONAS diseases, MEDLINE, DRUG resistance in microorganisms, STATISTICAL models, PROSTHESIS-related infections, EARLY diagnosis, DISEASE risk factors
Abstract

Background: Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection. Methods: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified. Results: Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis. Conclusion: Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Use of a modular hip dual-mobility articulation in patients with high risk of dislocation: a relatively small-sized acetabulum in Asian patients may limit its use

Author

Chan, Ping Keung, primary, Cheung, Sum Lik, additional, Lam, Kar Hei, additional, Fung, Wing Chiu, additional, Chan, Vincent Wai Kwan, additional, Cheung, Amy, additional, Cheung, Man Hong, additional, Fu, Henry, additional, Yan, Chun Hoi, additional, and Chiu, Kwong Yuen, additional

Published
2021
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