Purpose: Recently developed machine-learning algorithms have demonstrated strong performance in the detection of intracranial hemorrhage (ICH) and large vessel occlusion (LVO). However, their generalizability is often limited by geographic bias of studies. The aim of this study was to validate a commercially available deep learning-based tool in the detection of both ICH and LVO across multiple hospital sites and vendors throughout the U.S.Materials and Methods: This was a retrospective and multicenter study using anonymized data from two institutions. Eight hundred fourteen non-contrast CT cases and 378 CT angiography cases were analyzed to evaluate ICH and LVO, respectively. The tool's ability to detect and quantify ICH, LVO, and their various subtypes was assessed among multiple CT vendors and hospitals across the United States. Ground truth was based off imaging interpretations from two board-certified neuroradiologists.Results: There were 255 positive and 559 negative ICH cases. Accuracy was 95.6%, sensitivity was 91.4%, and specificity was 97.5% for the ICH tool. ICH was further stratified into the following subtypes: intraparenchymal, intraventricular, epidural/subdural, and subarachnoid with true positive rates of 92.9, 100, 94.3, and 89.9%, respectively. ICH true positive rates by volume [small (<5 mL), medium (5–25 mL), and large (>25 mL)] were 71.8, 100, and 100%, respectively. There were 156 positive and 222 negative LVO cases. The LVO tool demonstrated an accuracy of 98.1%, sensitivity of 98.1%, and specificity of 98.2%. A subset of 55 randomly selected cases were also assessed for LVO detection at various sites, including the distal internal carotid artery, middle cerebral artery M1 segment, proximal middle cerebral artery M2 segment, and distal middle cerebral artery M2 segment with an accuracy of 97.0%, sensitivity of 94.3%, and specificity of 97.4%.Conclusion: Deep learning tools can be effective in the detection of both ICH and LVO across a wide variety of hospital systems. While some limitations were identified, specifically in the detection of small ICH and distal M2 occlusion, this study highlights a deep learning tool that can assist radiologists in the detection of emergent findings in a variety of practice settings.

Twitter, the social media service that permits 140-character posts or "tweets," is undergoing rapid uptake by physicians. 1 Twitter allows physicians to communicate, interpret, highlight, and curate information as well as engage in discussion or debate with other physicians, patients, patient advocates, researchers, investors, and industry employees. More than 60% of tweets authored by medical professionals in the United States are health-related, and approximately 14% mention commercial products or services. 2 Yet, to our knowledge, there has been no investigation of the prevalence of financial conflict of interest (FCOI) among these users.Methods | We constructed a set of hematologist-oncologists who are active on Twitter and have primary affiliations in the United States. We then assessed their FCOIs occurring in 2014. This data set was created in 2 steps. First, we selected 1 hematologistoncologist (who does not have FCOI [V.P.]) and searched all Twitter users who were followed by and was following that physician; this set consisted of approximately 50 people. Second, we used Google to identify hematologist-oncologists with primary Related articles pages 352, 344 and 427

Cirrhosis and portal hypertension can lead to the formation of a spontaneous splenorenal shunt (SSRS) that may divert portal blood flow to the systemic circulation and reduce hepatic perfusion. Our aims were to evaluate SSRSs as an independent prognostic marker for mortality in patients with decompensated cirrhosis and the influence of SSRSs on liver transplantation (LT) outcomes. We retrospectively analyzed adult patients with decompensated cirrhosis undergoing LT evaluation from January 2001 to February 2016 at a large U.S. center. All patients underwent liver cross‐sectional imaging within 6 months of evaluation, and images were reviewed by two radiologists. Clinical variables were obtained by electronic health record review. The cohort was followed until death or receipt of LT, and the subset receiving LT was followed for death after LT or graft failure. Survival data were analyzed using multivariable competing risk and Cox proportional‐hazards regression models. An SSRS was identified in 173 (23%) of 741 included patients. Patients with an SSRS more often had portal vein thrombosis and less often had ascites (P < 0.01). An SSRS was independently associated with a nonsignificant trend for reduced mortality (adjusted subhazard ratio, 0.81; Gray's test P = 0.08) but had no association with receipt of LT (adjusted subhazard ratio, 1.02; Gray's test P = 0.99). Post‐LT outcomes did not differ according to SSRS for either death (hazard ratio, 0.85; log‐rank P = 0.71) or graft failure (hazard ratio, 0.71; log‐rank P = 0.43). Conclusion: Presence of an SSRS does not predict mortality in patients with decompensated cirrhosis or in LT recipients. (Hepatology Communications 2018;2:437‐444)

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PurposeAutomated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool’s impact on acute stroke workflow and clinical outcomes.Materials and methodsConsecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated.ResultsA total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI, p &lt; 0.0005), notably at the resident level (p &lt; 0.0003) but not at higher levels of expertise. There were no differences in door-to-treatment times, but the NIHSS at discharge was improved for the pre-AI group adjusted for confounders (parameter estimate = 3.97, p &lt; 0.01).ConclusionImplementation of an automated LVO detection tool improved radiology TAT but did not translate to improved stroke metrics and outcomes in a real-world setting.