Background The role and performance of chest CT in the diagnosis of the coronavirus disease 2019 (COVID-19) pandemic remains under active investigation. Purpose To evaluate the French national expe...

Objectives: To assess the impact of an Artificial Intelligence (AI) limb bone fracture diagnosis software (AIS) on emergency department (ED) workflow and diagnostic accuracy. Materials and Methods: A retrospective study was conducted in two phases—without AIS (Period 1: 1 January 2020–30 June 2020) and with AIS (Period 2: 1 January 2021–30 June 2021). Results: Among 3720 patients (1780 in Period 1; 1940 in Period 2), the discrepancy rate decreased by 17% (p = 0.04) after AIS implementation. Clinically relevant discrepancies showed no significant change (−1.8%, p = 0.99). The mean length of stay in the ED was reduced by 9 min (p = 0.03), and expert consultation rates decreased by 1% (p = 0.38). Conclusions: AIS implementation reduced the overall discrepancy rate and slightly decreased ED length of stay, although its impact on clinically relevant discrepancies remains inconclusive. Key Point: After AI software deployment, the rate of radiographic discrepancies decreased by 17% (p = 0.04) but this was not clinically relevant (−2%, p = 0.99). Length of patient stay in the emergency department decreased by 5% with AI (p = 0.03). Bone fracture AI software is effective, but its effectiveness remains to be demonstrated.

Introduction Fractures of the tibial plateau are in constant progression. They affect an elderly population suffering from a number of comorbidities, but also a young population increasingly practicing high-risk sports. The conventional open surgical technique used for tibial plateau fractures has several pitfalls: bone and skin devascularisation, increased risks of infection and functional rehabilitation difficulties. Since 2011, Poitiers University Hospital is offering to its patients a new minimally invasive technique for the reduction and stabilisation of tibial plateau fractures, named ‘tibial tuberoplasty’. This technique involves expansion of the tibial plateau through inflation using a kyphoplasty balloon, filling of the fracture cavity with cement and percutaneous screw fixation. We designed a study to evaluate the quality of fracture reduction offered by percutaneous tuberoplasty versus conventional open surgery for tibial plateau fracture and its impact on clinical outcome. Methods and analysis This is a multicentre randomised controlled trial comparing two surgical techniques in the treatment of tibial plateau fractures. 140 patients with a Schatzker II or III tibial plateau fracture will be recruited in France. They will be randomised either in tibial tuberoplasty arm or in conventional surgery arm. The primary outcome is the postoperative radiological step-off reduction blindly measured on CT scan (within 48 hours post-op). Additional outcomes include other radiological endpoints, pain, functional abilities, quality of life assessment and health-economic endpoints. Outcomes assessment will be performed at baseline (before surgery), at day 0 (surgery), at 2, 21, 45 days, 3, 6, 12 and 24 months postsurgery. Ethics and dissemination This study has been approved by the ethics committee Ile-De-France X and will be conducted in accordance with current Good Clinical Practice (GCP) guidelines, Declaration of Helsinki and standard operating procedures. The results will be disseminated through presentation at scientific conferences and publication in peer-reviewed journals. Trial registration number Clinicaltrial.gov:NCT03444779.

Background: Traumatic brain injury (TBI) is a major cause of morbimortality in the world, and it can cause potential intracranial hemorrhage (ICH), a life-threatening condition that requires rapid diagnosis with computed tomography (CT). Artificial intelligence tools for ICH detection are now commercially available. Objectives: Investigate the real-world performance of qER.ai, an artificial intelligence-based CT hemorrhage detection tool, in a post-traumatic population. Methods: Retrospective monocentric observational study of a dataset of consecutively acquired head CT scans at the emergency radiology unit to explore brain trauma. AI performance was compared to ground truth determined by expert consensus. A subset of night shift cases with the radiological report of a junior resident was compared to the AI results and ground truth. Results: A total of 682 head CT scans were analyzed. AI demonstrated a sensitivity of 88.8% and a specificity of 92.1% overall, with a positive predictive value of 65.4% and a negative predictive value of 98%. AI’s performance was comparable to that of junior residents in detecting ICH, with the latter showing a sensitivity of 85.7% and a high specificity of 99.3%. Interestingly, the AI detected two out of three ICH cases missed by the junior residents. When AI assistance was integrated, the combined sensitivity improved to 95.2%, and the overall accuracy reached 98.8%. Conclusions: This study shows better performance from AI and radiologist residents working together than each one alone. These results are encouraging for rethinking the radiological workflow and the future of triage of this large population of brain traumatized patients in the emergency unit.