@inproceeding{Steinmeister2020, Author = {L. Steinmeister, I. M. Baltruschat, H. Ittrich, A. Saalbach, H. Nickisch, M. Grass, T. Knopp and G. Adam}, Title = {Intelligent Chest X-Ray Worklist Prioritization by Deep Learning}, Journal = {European Congress of Radiology 2020}, Year = {2020}, Number = {C-07700}, Month = {January}, Note = {inproceeding}, Doi = {10.26044/ecr2020/C-07700}, Url = {http://dx.doi.org/10.26044/ecr2020/C-07700}, Keywords = {Artificial Intelligence and Machine Learning, Artificial Intelligence, eHealth, Thorax, Conventional radiography, Digital radiography, Neural networks, Computer Applications-Detection, diagnosis, Diagnostic procedure, Safety, Acute, Quality assurance, Workforce, Retrospective, Diagnostic or prognostic study, Performed at one institution}, Abstract = {Growing radiologic workload and shortage of medical experts worldwide often lead to delayed reads or even unreported examinations, which bears severe risks for patient’s safety (1,2). The aim of our study was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and could reduce report turnaround times (RTAT) for critical findings in chest radiographs (CXR), instead of reporting according to the First-In-First-Out-Principle (FIFO).} } @COMMENT{Bibtex file generated on 2021-7-28 with typo3 si_bibtex plugin. Data from /ibi/people/tobias-knopp-head-of-institute.html }