Slight differences in the percentage of titanium among the three pedicle screw systems does not appear to result in artifact differences during MR imaging. Therefore, with regard to imaging profile considerations, the three systems studied should be considered interchangeable.

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The etiology of depression in the elderly is poorly understood. In this study, magnetic resonance imaging was used to evaluate the role of subcortical structures in the pathophysiology of depression in the elderly. Elderly depressed patients were found to have smaller caudate nuclei, smaller putaminal complexes and in increased frequency of subcortical hyperintensities compared with normal, healthy controls. These findings were more pronounced in patients with late-onset depression. Based on these findings, the authors discuss the role of the basal ganglia in the pathophysiology of depression in the elderly.

IMPORTANCE Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. OBJECTIVE To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set