Background Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. Methods PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: ‘artificial intelligence’, ‘machine learning’, ‘knee’, ‘osteoarthritis’, and ‘arthroplasty’. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. Results Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. Conclusions Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.

Background Surgical site infection following joint replacement surgery is still a significant complication, resulting in repeated surgery, prolonged antibiotic therapy, extended postoperative hospital stay, periprosthetic joint infection, and increased morbidity and mortality. This review discusses the risk factors associated with surgical site infection. Related risk factors The patient-related factors include sex, age, body mass index (BMI), obesity, nutritional status, comorbidities, primary diagnosis, living habits, and scores of the American Society of Anesthesiologists physical status classification system, etc. Surgery-related factors involve preoperative skin preparation, prolonged duration of surgery, one-stage bilateral joint replacement surgery, blood loss, glove changes, anti-microbial prophylaxis, topical anti-bacterial preparations, wound management, postoperative hematoma, etc. Those risk factors are detailed in the review. Conclusion Preventive measures must be taken from multiple perspectives to reduce the incidence of surgical site infection after joint replacement surgery.

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.

The usage of telemedicine and telehealth services has grown tremendously and has become increasingly relevant and essential. Technological advancements in current telehealth services have supported its use as a viable alternative tool to conduct visits for consultations, follow-up, and rehabilitation in total joint arthroplasty. Such technology has been widely implemented, particularly during the coronavirus 2019 (COVID-19) pandemic, to deliver postoperative rehabilitation among patients receiving total joint arthroplasty (TJA), further demonstrating its feasibility with a lower cost yet comparable clinical outcomes when compared with traditional care. There remains ample potential to utilize telemedicine for prehabilitation to optimize the preoperative status and postoperative outcomes of patients with osteoarthritis. In this review, various implementations of telemedicine within total joint arthroplasty and future application of telemedicine to deliver tele-prehabilitation in TJA are discussed.

Current practice of osteoarthritis has its insufficiencies which researchers are tackling with artificial intelligence (AI). This article discusses three kinds of AI models, namely diagnostic models, prediction models and morphological models. Diagnostic models enhance efficiency in diagnosis by providing an automated algorithm in knee images processing. Prediction models utilize behavioral and radiological data to assess the risk of osteoarthritis before symptom onset and needs to perform surgery. Morphological models detect biomechanical changes to facilitate understanding of pathophysiology and provide personalized intervention. Through reviewing present evidence, we demonstrate that AI could assist doctors in diagnosis, predict osteoarthritis and guide future research.

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.

Background Perioperative antibiotics is a well established measure to prevent periprosthetic joint infection (PJI). However, the usual practice of prescription vary from surgeon to surgeon. This study aims to investigate and compare the usual practice and variability of prescribing antibiotic prophylaxis among different joint replacement surgeons in Hong Kong. Methods An online questionnaire was prepared and sent to members of the Adult Joint Reconstruction (AJR) Chapter of the Hong Kong Orthopaedic Association (HKOA). The questionnaire consists of a total of 15 questions in 3 categories: choice of antibiotics, dose of antibiotics and duration of antibiotics prescribed by the surgeon. Results A total of 25 responses were received and data collected. Participants were from a diverse background from different hospitals with variable years of experience. Results showed a general consensus on the choice of antibiotics, but also a wide variability on the actual prescription, particularly about the weight-adjusted dose and total duration of antibiotics given. Conclusion There is a wide variability among surgeons regarding the actual prescription of antibiotic prophylaxis. Correlation between rate of PJI and specific aspects of antibiotic prescription is needed to give recommendations to surgeons regarding perioperative antibiotic usage in total joint arthroplasties.