While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

Objectives CT-guided radiofrequency ablation (CT-RFA) is considered to be the gold standard for treatment of osteoid osteoma (OO) yet treatment failures (TFs) continue to be reported. This systematic review was conducted to evaluate factors associated with TF, such as ablation time, lesion location, and patient age as well as evaluating how TF has trended over time. Methods Original studies reporting on patients undergoing CT-RFA of OO published between 2002 and 2019 were identified. TF was defined as patients with (1) recurrent or persistent pain +/− (2) imaging evidence of persistent OO. TFs were subdivided into those occurring after the index procedure (primary TF) or those occurring after repeat RFA (secondary TF). Subgroup analysis was performed for TF based on the study date (2002–2010 or 2010–2019), time duration of ablation at 90 °C (6 min or > 6 min), patient age, and tumor location (spinal vs. appendicular). Results Sixty-nine studies were included for a total of 3023 patients. The global primary TF rate was 8.3% whereas the secondary TF rate was 3.1%. The TF rate reported in studies published after 2011(7%) was about half that during the earlier time period 2002–2010 (14%). There was no statistical difference in TF corrected for age, OO location, or duration of ablation (respectively p = 0.39, 0.13, and 0.23). The global complication rate was 3%, the most frequent being skin burns (n = 24; 0.7%). Conclusions A decrease in TF observed between 2011–2019 compared to 2002–2010 may reflect improvements in operator technique or advancements in equipment. Duration of ablation, patient age, or location of OO failed to significantly correlate with TF. Key Points • CT-guided radiofrequency ablation of osteoid osteomas is a safe technique with a low rate of treatment failure (8.3% failure rate after the primary radiofrequency reducing to 3.1% following a secondary treatment). • The treatment failure rate has decreased over time, possibly due to an improved understanding of the disease process, better technique, and advances in equipment. • Duration of ablation, patient age, or lesion location did not significantly correlate with treatment failure.

Introduction: In numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection. Methods: This multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection. Results: 605 patients were included between March 10^th and April 30^th, 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (<1.3 G/L), eosinophil (<0.06 G/L), basophil (<0.04 G/L) and neutrophil counts (<5 G/L) were associated with high probability of SARS-CoV-2 infection but no clinical variable was statistically significant. The score had a good performance in the validation cohort (AUC = 0.918 (CI: [0.891–0.946]; STD = 0.014) with a Positive Predictive Value of high-probability score of 93% (95%CI: [0.89–0.96]). Furthermore, a low-probability score excluded SARS-CoV-2 infection with a Negative Predictive Value of 98% (95%CI: [0.93–0.99]). The performance of the score was stable even during the last period of the study (15-30^th April) with more controls than infected patients. Conclusions: The PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.