BACKGROUND AND PURPOSE:Following mechanical recanalization of an acute intracranial vessel occlusion, hyperattenuated lesions are frequently found on postinterventional cranial CT. They represent either blood or-more frequently-enhancement of contrast agent. Here, we aimed to evaluate the prognostic value of these hyperattenuated intracerebral lesions.

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Conservative management of ruptured BA aneurysms might be a first-line treatment option with common spontaneous aneurysm occlusion, low rate of re-SAH, and promising clinical outcome.

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Objective C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD). We examined aging trajectories of cortical thickness (CTh) and surface area in C9orf72 expansion adult carriers compared to healthy controls to characterize preclinical cerebral changes leading to symptoms. Methods Data were obtained from the Genetic Frontotemporal Dementia Initiative. T1‐weighted magnetic resonance imaging scans were processed with CIVET 2.1 to extract vertex‐wide CTh and cortical surface area (CSA). Symptomatic and presymptomatic subjects were compared to age‐matched controls using mixed‐effects models, controlling for demographic variables. Aging trajectories were compared between carriers and noncarriers by testing the “age by genetic status” interaction. False discovery rate corrections were applied to all vertex‐wide analyses. Results The sample included 640 scans from 386 subjects, including 54 symptomatic C9orf72 carriers (72.2% behavioral variant FTD), 83 asymptomatic carriers, and 249 controls (age range = 18–86 years). Symptomatic carriers showed fairly symmetric reduction in CTh/CSA in most of the frontal lobes, in addition to large temporoparietal areas. Presymptomatic subjects had reduced CTh/CSA in more restricted areas of the medial frontoparietal lobes, in addition to scattered lateral frontal, parietal, and temporal areas. These differences were explained by faster cortical thinning linearly throughout adulthood in a similar anatomical distribution, with differences emerging in the early 30s. CSA reduction was also faster in mutation carriers predominantly in the ventrofrontal regions. Interpretation C9orf72 mutation carriers have faster cortical thinning and surface loss throughout adulthood in regions that show atrophy in symptomatic subjects. This suggests that the pathogenic effects of the mutation lead to structural cerebral changes decades prior to symptoms. ANN NEUROL 2020 ANN NEUROL 2020;88:113–122

Objectives: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. Design: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. Patients: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. Measurements and Main Results: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78–0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist’s maximum sensitivity up to 0.87 (95% CI, 0.76–0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659–0.815) versus radiologist’s area under the receiver-operating characteristic curve of 0.779 (0.723–0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist’s and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662–0.817) versus radiologist’s area under the receiver-operating characteristic curve of 0.698 (0.646–0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. Conclusions: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence–assisted reading setting.

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Our findings suggest that preceding intravenous thrombolysis may reduce the intervention time in patients treated by endovascular mechanical recanalization. However, due to the retrospective design of our study, these findings have to be interpreted with caution and need confirmation in a larger patient population.