The aim of this study was to evaluate the number of primary and revision total joint arthroplasties (TJA/rTJA) in 2020 compared to 2019. Specifically, the first and the second waves of the COVID-19 pandemic were evaluated as well as the pre-operative COVID-19 test. A cross-sectional single-center study of our prospectively maintained institutional arthroplasty registry was performed. The first COVID-19 wave and the second COVID-19 wave led to a socioeconomic lockdown in 2020. Performed surgeries, cause of revision, age, gender, and American Society of Anesthesiologists-level were analyzed. Preoperative COVID-19 testing was evaluated and nationwide COVID-19 data were compared to other countries. In 2020, there was a decrease by 16.2% in primary and revision TJAs of the hip and knee compared to 2019. We observed a reduction of 15.8% in primary TJAs and a reduction of 18.6% on rTJAs in 2020 compared to 2019. There is an incline in total hip arthroplasties (THAs) and a decline in total knee arthroplasties (TKAs) comparing 2019 to 2020. During the first wave, there was a reduction in performed primary TJAs of 86%. During the second wave, no changes were observed. This is the first study quantifying the impact of the COVID-19 pandemic on primary and revision TJAs regarding the first and second wave.
Purpose The purpose of this study was to evaluate the reliability of a newly developed AI-algorithm for the evaluation of long leg radiographs (LLR) after total knee arthroplasties (TKA). Methods In the validation cohort 200 calibrated LLRs of eight diferent common unconstrained and constrained knee systems were analysed. Accuracy and reproducibility of the AI-algorithm were compared to manual reads regarding the hipknee-ankle (HKA) as well as femoral (FCA) and tibial component (TCA) angles. In the evaluation cohort all institutional LLRs with TKAs in 2018 (n = 1312) were evaluated to assess the algorithms' ability of handling large data sets. Intraclass correlation (ICC) coeicient and mean absolute deviation (sMAD) were calculated to assess conformity between the AI software and manual reads. Results Validation cohort: The AI-software was reproducible on 96% and reliable on 92.1% of LLRs with an output and showed excellent reliability in all measured angles (ICC > 0.97) compared to manual measurements. Excellent results were found for primary unconstrained TKAs. In constrained TKAs landmark setting on the femoral and tibial component failed in 12.5% of LLRs (n = 9). Evaluation cohort: Mean measurements for all postoperative TKAs (n = 1240) were 0.2° varus ± 2.5° (HKA), 89.3° ± 1.9° (FCA), and 89.1° ± 1.6° (TCA). Mean measurements on preoperative revision TKAs (n = 74) were 1.6 varus ± 6.4° (HKA), 90.5° ± 3.1° (FCA), and 88.9° ± 4.1° (TCA). Conclusions AI-powered applications are reliable for automated analysis of lower limb alignment on LLRs with TKAs. They are capable of handling large data sets and could, therefore, lead to more standardized and eicient postoperative quality controls. Level of evidence Diagnostic Level III.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.