Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety.
Flagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 72 - 81 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| 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=175966523&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966523 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966523 175966523 175966523 10.1007/s10278-023-00932-8 175966523 ppf: 72 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety. aug: au: Courtman, Megan Kim, Daniel Wit, Huub Wang, Hongrui Sun, Lingfen Ifeachor, Emmanuel Mullin, Stephen Thurston, Mark affil: https://ror.org/008n7pv89 Faculty of Science and Engineering, School of Engineering, Computing and Mathematics, University of Plymouth, PL4 8AA, Plymouth, UK sug: subj: Deep Learning Aneurysm Surgery Surgical Instruments Magnetic Resonance Imaging Human Funding Source Tomography, X-Ray Computed Artificial Intelligence Neural Networks (Computer) Patient Safety ab: Flagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence or absence of an aneurysm clip on CT images. A total of 280 CT head scans were collected, 140 with aneurysm clips visible and 140 without. The data were used to retrain a pre-trained image classification neural network to classify CT localizer images. Models were developed using fivefold cross-validation and then tested on a holdout test set. A mean sensitivity of 100% and a mean accuracy of 82% were achieved. Predictions were explained using SHapley Additive exPlanations (SHAP), which highlighted that appropriate regions of interest were informing the models. Models were also trained from scratch to classify three-dimensional CT head scans. These did not exceed the sensitivity of the localizer models. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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