Neural Network Detection of Pacemakers for MRI Safety.
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 radi...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1673 - 1681 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160503242&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503242 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503242 157711415 160503242 160503242 10.1007/s10278-022-00663-2 160503242 ppf: 1673 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Neural Network Detection of Pacemakers for MRI Safety. aug: au: Thurston, Mark Daniel Vernon Kim, Daniel H Wit, Huub K affil: Peninsula Medical School, University of Plymouth, Plymouth Science Park, PL6 8BT, Plymouth, UK sug: subj: Neural Networks (Computer) Pacemaker, Artificial Magnetic Resonance Imaging Patient Safety Evaluation Machine Learning Radiography, Thoracic Human Heart Assist Devices Radiology Information Systems Descriptive Statistics Artificial Intelligence Questionnaires ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|