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.