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dc.contributor.authorThurston, Mark
dc.contributor.authorWit, H
dc.contributor.authorKim, D
dc.date.accessioned2022-06-08T15:17:26Z
dc.date.issued2022-12
dc.identifier.issn0897-1889
dc.identifier.issn1618-727X
dc.identifier.urihttp://hdl.handle.net/10026.1/19284
dc.description.abstract

<jats:title>Abstract</jats:title><jats:p>Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-existing chest radiographs. A total of 7973 chest radiographs were collected, 3996 with pacemakers visible and 3977 without. Images were identified from information available on the radiology information system (RIS) and correlated with report text. Manual review of images by two board certified radiologists was performed to ensure correct labeling. The data set was divided into training, validation, and a hold-back test set. The data were used to retrain a pre-trained image classification neural network. Final model performance was assessed on the test set. Accuracy of 99.67% on the test set was achieved. Re-testing the final model on the full training and validation data revealed a few additional misclassified examples which are further analyzed. Neural network image classification could be used to screen for the presence of cardiac devices, in addition to current safety processes, providing notification of device presence in advance of safety questionnaires. Computational power to run the model is low. Further work on misclassified examples could improve accuracy on edge cases. The focus of many healthcare applications of computer vision techniques has been for diagnosis and guiding management. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.</jats:p>

dc.format.extent1673-1680
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoen
dc.publisherSpringer
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectCardiac devices
dc.subjectImage classification
dc.subjectMRI
dc.subjectPatient safety
dc.titleNeural network detection of pacemakers for MRI safety
dc.typejournal-article
dc.typeJournal Article
dc.typeResearch Support, Non-U.S. Gov't
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000818660600004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume35
plymouth.publication-statusPublished
plymouth.journalJournal of Digital Imaging
dc.identifier.doi10.1007/s10278-022-00663-2
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Users by role
dc.publisher.placeUnited States
dcterms.dateAccepted2022-05-30
dc.rights.embargodate9999-12-31
dc.identifier.eissn1618-727X
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1007/s10278-022-00663-2
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review


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