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T he accurate determination of a child's developmental status is required for proper treatment of various growth disorders (1) and scoliosis (2). Other parameters, such as height, weight, secondary sexual characteristics, chronologic age, and dental age, correlate with developmental status, but skeletal age has been considered the most reliable method (3-5). The standard of care for this assessment calls for radiologists to identify the reference standard in an atlas of hand radiographs that most closely resembles an anteroposterior or posteroanterior radiograph of the participant's left hand. The most common atlas used as a reference standard is the Radiographic Atlas of Skeletal Development of the Hand and Wrist, published in 1959 (6).As part of the process of implementing an artificial intelligence (AI) algorithm in clinical practice, it is critical to properly determine its effects. However, different study designs may yield different findings about the same assistive technologies. For example, the same commercially available computer-aided detection system for detecting pulmonary nodules on chest CT scans produced different findings in studies completed within a year of each other (7-9). Findings on potential computer-aided diagnosis Background: Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice.Purpose: To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods:In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center-and radiologist-level effects was used to compare the two experimental groups.Results: Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion:Use of an artificial intelligence algorithm ...

Magnetic resonance cholangiopancreatography (MRCP) is commonly performed in the evaluation of known or suspected pancreaticobiliary disease in children. The administration of a negative oral contrast agent can improve the quality of the examination without significant additional cost. We describe our experience with certain brands of acai juice, blueberry juice and pineapple juice as negative oral contrast agents in children. We believe these fruit juices are safe, palatable and may improve MRCP image quality.

Rationale and Objectives The educational value of the daily resident readout, a vital component of resident training, has been markedly diminished due to a significant decrease in imaging volume and case mix diversity. The goal of this study was to create a “simulated” daily readout (SDR) to restore the educational value of the daily readout. Materials and Methods To create the SDR the following tasks were performed; selection of cases for a daily worklist for each resident rotation, comprising a combination of normal and abnormal cases; determination of the correct number of cases and the appropriate mix of imaging modalities for each worklist; development of an "educational" environment consisting of separate "instances" of both our Picture Archive Communication System and reporting systems; and the anonymization of all of the cases on the worklists. Surveys of both residents and faculty involved in the SDR were performed to assess its effectiveness. Results Thirty-two residents participated in the SDR. The daily worklists for the first 20 days of the SDR included 3682 cases. An average of 480 cases per day was dictated by the residents. Surveys of the residents and the faculty involved in the SDR demonstrated that both agreed that the SDR effectively mimics a resident's daily work on rotations and preserves resident education during the Coronavirus Disease 2019 crisis. Conclusion The development of the SDR provided an effective method of preserving the educational value of the daily readout experience of radiology residents, despite severe decreases in imaging exam volume and case mix diversity during the Coronavirus Disease 2019 pandemic.