Perception from students regarding online synchronous interactive teaching in the clinical year during COVID-19 pandemic.

AIM: The global pandemic of COVID-19 has led to extensive practice of online learning. Our main objective is to compare different online synchronous interactive learning activities to evaluate students' perceptions. Moreover, we also aim to identify factors influencing their perceptions in these classes. METHODS: A cross-sectional, questionnaire-based study focusing on clinical year medical students' perceptions and feedback was conducted between February 2021 -June 2021 at the University of Hong Kong. Online learning activities were divided into bedside teaching, practical skill session, problem-based learning (PBL) or tutorial, and lecture. A questionnaire based on the Dundee Ready Education Environment Measure (DREEM) was distributed to 716 clinical year students to document their perceptions. RESULTS: One hundred responses were received with a response rate of 15.4% (110/716, including 96 from bedside teaching, 67 from practical skill session, 104 from PBL/tutorial, and 101 from lecture). For the mean score of the DREEM-extracted questionnaire, online PBL/tutorial scored the highest (2.72 ± 0.54), while bedside scored the lowest (2.38 ± 0.68, p = 0.001). Meanwhile, there was no significant difference when we compared different school years (p = 0.39), age (p = 0.37), gender (p = 1.00), year of internet experience (<17 vs ≥17 years p = 0.59), or prior online class experience (p = 0.62). When asked about students' preference for online vs face-to-face classes. Students showed higher preferences for online PBL/tutorial (2.06 ± 0.75) and lectures (2.27 ± 0.81). Distraction remains a significant problem across all four learning activities. A multivariate analysis was performed regarding students' reported behavior in comparison with their perception through the DREEM-extracted questionnaire. The results showed that good audio and video quality had a significant and positive correlation with their perception of online bedside teaching, practical skill sessions, and PBL/tutorial. It also showed that the use of the video camera correlated with an increase in perception scores for lectures. CONCLUSION: The present analysis has demonstrated that students' perception of different online synchronous interactive learning activities varies. Further investigations are required on minimizing distraction during online classes.

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review.

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.

Imaging of knee osteoarthritis: A review of current evidence and clinical guidelines.

BACKGROUND: Knee osteoarthritis (OA) is one of the most common and debilitating degenerative joint diseases worldwide. While radiography is the most commonly used imaging modality, it is associated with drawbacks which newer modalities such as magnetic resonance imaging (MRI) and ultrasound could overcome. Nevertheless, the role of imaging in clinical practice and research in knee OA has not been clearly defined. Furthermore, guidelines on imaging in knee OA from different authoritative bodies have not been compared in previous studies. Therefore, the present review aims to summarise existing evidence and compare guidelines on the use of different imaging modalities in evaluating knee OA. METHODS: This is a narrative review based on a search of published clinical guidelines and the PubMed database for articles published between 1 January 1990 and 31 May 2020. RESULTS: There is no broad consensus on the value of imaging in patients with typical OA presentation. If imaging is required, current evidence and clinical guidelines support the use of radiography and MRI as first- and second-line diagnostic modalities respectively. Since radiographic OA features have limited sensitivity and do not manifest in early stages, MRI is the preferred option for whole-joint evaluation in OA research. Discrepancies exist regarding the use of alternative imaging modalities including ultrasound, computed tomography and nuclear medicine. CONCLUSION: Radiography and MRI are the imaging modalities of choice. Other modalities have their respective advantages, and more research is warranted for the standardisation of image acquisition and interpretation methodology, in order to evaluate their validity, reliability and responsiveness in OA research.

Lessons learnt from the impact of COVID-19 on arthroplasty services in Hong Kong: how to prepare for the next pandemic?

BACKGROUND: Arthroplasty services worldwide have been significantly disrupted by the pandemic of coronavirus disease 2019 (COVID-19). This retrospective comparative study aimed to characterize its impact on arthroplasty services in Hong Kong. METHODS: From January 1 to June 30, 2020, the patients of "COVID-19 cohort" underwent elective total hip or knee replacement in Hong Kong public hospitals. The cohort was compared to the "control cohort" during the same period in 2019. Data analysis was performed to compare the two cohorts' numbers of operations, hospital admission, orthopaedic clinic attendances, and waiting time. RESULTS: A total of 33,111 patient episodes were analyzed. During the study period, the elective arthroplasty operations and hospitalizations decreased by 53 and 54%, respectively (P < 0.05). Reductions were most drastic from February to April, with surgical volume declining by 86% (P < 0.05). The primary arthroplasty operations decreased by 91% (P < 0.05), while the revision operations remained similar. Nevertheless, 14 public hospitals continued performing elective arthroplasty for patients with semi-urgent indications, including infection, progressive bone loss, prosthesis loosening, dislocation or mechanical failure of arthroplasty, and tumor. At the institution with the highest arthroplasty surgical volume, infection (28%) was the primary reason for surgery, followed by prosthesis loosening (22%) and progressive bone loss (17%). The orthopaedic clinic attendances also decreased by 20% (P < 0.05). Increases were observed in waiting time and the total number of patients on the waiting list for elective arthroplasty. CONCLUSIONS: Despite the challenges, public hospitals in Hong Kong managed to continue providing elective arthroplasty services for high-priority patients. Arthroplasty prioritization, infection control measures, and post-pandemic service planning can enhance hospital preparedness to mitigate the impact of current and future pandemics.