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Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents' performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents' sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI's potential in radiology, emphasizing its role in training and interpretation improvement.
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OBJECTIVES: The Manchester Triage System (MTS) has entered widespread international use in emergency departments (EDs). This retrospective study analyzes urgency of patient visits (PV) at the ED of the Clinic for Pediatrics at the Medical University of Innsbruck. METHODS: We collected demographic and outcome information, including PV urgency levels (UL) according to the MTS, for 3 years (2015-2018), separating PV during regular office hours (ROH; 8:00 am to 5:00 pm) from PV during afternoon and night hours (5:00 pm to 8:00 am), and PV on weekdays from PV on weekends and bank holidays (WE). RESULTS: A total of 56,088 PV were registered with a UL. Most (68.4%) PV were classified as nonurgent. During ROH, more PV per hour (PV/h) were recorded than during afternoon and night hours (3.0 PV/h vs 1.6 PV/h), with a higher proportion of less urgent cases during ROH. On WE, the amount of PV/h was higher than on weekdays (3.6 PV/h vs 2.8 PV/h), with a higher proportion of nonurgent cases (74.6% vs 68.6%). Likelihoods of inpatient admission and hospital stay lengths increased in step with UL. CONCLUSIONS: The MTS proved useful for delineating UL distributions. The MTS analyses may be of value in managing EDs. Prompted by the results of our study, a general practice pediatric care unit was established to support the ED during WE.
