Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3575
DC FieldValueLanguage
dc.contributor.authorBaltruschat, Ivo-Matteo-
dc.contributor.authorSteinmeister, Leonhard A.-
dc.contributor.authorNickisch, Hannes-
dc.contributor.authorSaalbach, Axel-
dc.contributor.authorGrass, Michael-
dc.contributor.authorAdam, Gerhard-
dc.contributor.authorKnopp, Tobias-
dc.contributor.authorIttrich, Harald-
dc.date.accessioned2020-12-03T12:40:33Z-
dc.date.available2020-12-03T12:40:33Z-
dc.date.issued2020-11-21-
dc.identifier.citationEuropean Radiology 31 (6): 3837-3845 (2021)de_DE
dc.identifier.issn0938-7994de_DE
dc.identifier.urihttp://hdl.handle.net/11420/8121-
dc.description.abstractObjective: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI—resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist. Methods: We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing “first-in, first-out” (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination. Results: The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our “upper limit” substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001). Conclusion: Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO. Key Points: • Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence. • Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time. • Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).en
dc.language.isoende_DE
dc.publisherSpringerde_DE
dc.relation.ispartofEuropean radiologyde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectArtificial intelligencede_DE
dc.subjectRadiographyde_DE
dc.subjectWaiting listsde_DE
dc.subjectWorkflowde_DE
dc.subject.ddc004: Informatikde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc610: Medizinde_DE
dc.titleSmart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulationde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.3575-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0116889-
tuhh.oai.showtruede_DE
tuhh.abstract.englishObjective: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI—resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist. Methods: We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing “first-in, first-out” (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination. Results: The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our “upper limit” substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001). Conclusion: Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO. Key Points: • Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence. • Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time. • Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).de_DE
tuhh.publisher.doi10.1007/s00330-020-07480-7-
tuhh.publication.instituteBiomedizinische Bildgebung E-5de_DE
tuhh.identifier.doi10.15480/882.3575-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue6de_DE
tuhh.container.volume31de_DE
tuhh.container.startpage3837de_DE
tuhh.container.endpage3845de_DE
dc.relation.projectProjekt DEAL-
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85096382871de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.creatorOrcidBaltruschat, Ivo-Matteo-
item.creatorOrcidSteinmeister, Leonhard A.-
item.creatorOrcidNickisch, Hannes-
item.creatorOrcidSaalbach, Axel-
item.creatorOrcidGrass, Michael-
item.creatorOrcidAdam, Gerhard-
item.creatorOrcidKnopp, Tobias-
item.creatorOrcidIttrich, Harald-
item.languageiso639-1en-
item.creatorGNDBaltruschat, Ivo-Matteo-
item.creatorGNDSteinmeister, Leonhard A.-
item.creatorGNDNickisch, Hannes-
item.creatorGNDSaalbach, Axel-
item.creatorGNDGrass, Michael-
item.creatorGNDAdam, Gerhard-
item.creatorGNDKnopp, Tobias-
item.creatorGNDIttrich, Harald-
item.openairetypeArticle-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.mappedtypeArticle-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
crisitem.author.deptBiomedizinische Bildgebung E-5-
crisitem.author.deptBiomedizinische Bildgebung E-5-
crisitem.author.orcid0000-0002-8748-3820-
crisitem.author.orcid0000-0003-1604-6647-
crisitem.author.orcid0000-0002-1589-8517-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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