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https://www.selleckchem.com/products/lly-283.html Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accuracy and triage. This research charts the potential of deep learning applied to the analysis of CTB scans. It draws on the experience of practicing clinicians and technologists involved in development and implementation of deep learning-based clinical decision support systems. We consider the past, present and future of the CTB, along with limitations of existing systems as well as untapped beneficial use cases. Implementing deep learning CTB interpretation systems and effectively navigating development and implementation risks can deliver many benefits to clinicians and patients, ultimately improving efficiency and safety in healthcare. To explore prognostic factors of complete recovery of oculomotor nerve palsy (ONP) induced by posterior communicating artery aneurysm (PcomAA). PcomAA patients aged 18-60years combined with ONP who underwent surgical clipping or endovascular embolization at our institution between January 2014 and January 2020 were enrolled. Characteristics included maximum diameter of aneurysm, width of aneurysm, subarachnoid hemorrhage (SAH), duration of ONP, age, sex, ONP type, treatment method were compared. Based on the recovery of ONP, patients were separated into two groups complete recovery group, partial and no recovery group. Analyzing by univariate and multivariate logistic regressions to identify the independent prognostics for complete ONP recovery. We established a score based on these prognostics. Receiver operating characteristics (ROC) were conducted to under the performance of the predictors and score. Finally, ONP type (OR 6.457 95% CI 1.6

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