{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T12:26:30Z","timestamp":1751286390041,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/T518153\/1"],"award-info":[{"award-number":["EP\/T518153\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["PEN\/006\/005\/A"],"award-info":[{"award-number":["PEN\/006\/005\/A"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1007\/s10278-023-00932-8","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T18:02:28Z","timestamp":1705082548000},"page":"72-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8984-7798","authenticated-orcid":false,"given":"Megan","family":"Courtman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huub","family":"Wit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongrui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel","family":"Ifeachor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Mullin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Thurston","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"932_CR1","doi-asserted-by":"crossref","unstructured":"J. T. McFadden,\u00a0\u201cMagnetic resonance imaging and aneurysm clips: a review,\u201d Journal of neurosurgery, vol. 117, no. 1, pp. 1-11, 2012.","DOI":"10.3171\/2012.1.JNS111786"},{"key":"932_CR2","doi-asserted-by":"crossref","unstructured":"M. F. Dempsey, B. Condon, and D. M.\u00a0Hadley,\u00a0\u201cMRI safety review,\u201d in Seminars in Ultrasound, CT and MRI, 2002, vol. 23, no. 5, pp. 392-401.","DOI":"10.1016\/S0887-2171(02)90010-7"},{"key":"932_CR3","doi-asserted-by":"crossref","unstructured":"R. P. Klucznik, D. A. Carrier, R. Pyka, and R. W. Haid,\u00a0\u201cPlacement of a ferromagnetic intracerebral aneurysm clip in a magnetic field with a fatal outcome.,\u201d Radiology, vol. 187, no. 3, pp. 855-856, 1993.","DOI":"10.1148\/radiology.187.3.8497645"},{"key":"932_CR4","doi-asserted-by":"crossref","unstructured":"A. Cunqueiro, M. Lipton, R. Dym, V. Jain, J. Sterman, and M. Scheinfeld, \u201cPerforming MRI on patients with MRI-conditional and non-conditional cardiac implantable electronic devices: an update for radiologists,\u201d Clinical Radiology, vol. 74, no. 12, pp. 912-917, 2019.","DOI":"10.1016\/j.crad.2019.07.006"},{"key":"932_CR5","doi-asserted-by":"crossref","unstructured":"F. G. Shellock and A. Spinazzi, \u201cMRI safety update 2008: part 2, screening patients for MRI.,\u201d American Journal of Roentgenology, vol. 191, no. 4, p. 1140, 2008.","DOI":"10.2214\/AJR.08.1038.2"},{"key":"932_CR6","doi-asserted-by":"crossref","unstructured":"A. Esteva et\u00a0al., \u201cDeep learning-enabled medical computer vision,\u201d npj Digital Medicine, vol. 4, no. 1, pp. 1-9, 2021.","DOI":"10.1038\/s41746-020-00376-2"},{"key":"932_CR7","unstructured":"I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016."},{"key":"932_CR8","doi-asserted-by":"crossref","unstructured":"Y. Bengio et\u00a0al., \u201cLearning deep architectures for AI,\u201d Foundations and trends\u00ae in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009.","DOI":"10.1561\/2200000006"},{"key":"932_CR9","doi-asserted-by":"crossref","unstructured":"A. Krizhevsky, I. Sutskever, and G. E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.","DOI":"10.1145\/3065386"},{"key":"932_CR10","doi-asserted-by":"crossref","unstructured":"G. Litjens et\u00a0al., \u201cA survey on deep learning in medical image analysis,\u201d Medical image analysis, vol. 42, pp. 60-88, 2017.","DOI":"10.1016\/j.media.2017.07.005"},{"key":"932_CR11","unstructured":"OFFIS, \u201cDCMTK,\u201d available via https:\/\/dicom.offis.de\/dcmtk\/. Accessed October 2023."},{"key":"932_CR12","unstructured":"Radiological Society of North America, Inc., \u201cCTP - The RSNA Clinical Trial Processor,\u201d available via https:\/\/mircwiki.rsna.org\/index.php?title=MIRC_CTP. Accessed October 2023."},{"key":"932_CR13","unstructured":"G. Bradski, and A. Kaehler, \u201cOpenCV,\u201d Dr. Dobb\u2019s journal of software tools, 3(2), 2000."},{"key":"932_CR14","unstructured":"P. Virtanen et\u00a0al., \u201cSciPy 1.0: fundamental algorithms for scientific computing in Python,\u201d Nature methods, 17(3), pp.261-272,2020."},{"key":"932_CR15","doi-asserted-by":"crossref","unstructured":"S. Van der Walt et\u00a0al., \u201cscikit-image: image processing in Python,\u201d PeerJ, 2, p.e453, 2014.","DOI":"10.7717\/peerj.453"},{"key":"932_CR16","unstructured":"F. Chollet and others,\u201c Keras.\u201d 2015."},{"key":"932_CR17","unstructured":"M. Abadi et\u00a0al., \u201cTensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.\u201d 2015."},{"key":"932_CR18","unstructured":"T. Kluyver et\u00a0al., \u201cJupyter Notebooks - a publishing format for reproducible computational workflows,\u201d in Positioning and Power in Academic Publishing: Players, Agents and Agendas, 2016, pp. 87-90."},{"key":"932_CR19","unstructured":"K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"932_CR20","doi-asserted-by":"crossref","unstructured":"C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, \u201cRethinking the inception architecture for computer vision,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"932_CR21","doi-asserted-by":"crossref","unstructured":"F. Chollet, \u201cXception: Deep learning with depthwise separable convolutions,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"932_CR22","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, \u201cDensely connected convolutional networks,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"932_CR23","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, \u201cMobilenetv2: Inverted residuals and linear bottlenecks,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510-4520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"932_CR24","unstructured":"S. Chen, K. Ma, and Y. Zheng, \u201cMed3d: Transfer learning for 3d medical image analysis,\u201d arXiv preprint arXiv:1904.00625, 2019."},{"key":"932_CR25","unstructured":"D. P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"932_CR26","unstructured":"S. M. Lundberg and S.-I. Lee, \u201cA Unified Approach to Interpreting Model Predictions,\u201d in Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp."},{"key":"932_CR27","doi-asserted-by":"crossref","unstructured":"M. D. V. Thurston, D. H. Kim, and H. K. Wit, \u201cNeural network detection of pacemakers for MRI safety,\u201d Journal of Digital Imaging, vol. 35, no. 6, pp. 1673-1680, 2022.","DOI":"10.1007\/s10278-022-00663-2"},{"key":"932_CR28","doi-asserted-by":"crossref","unstructured":"H.-S. Yang, K.-R. Kim, S. Kim, and J.-Y. Park, \u201cDeep learning application in spinal implant identification,\u201d Spine, vol. 46, no. 5, pp. E318-E324, 2021.","DOI":"10.1097\/BRS.0000000000003844"},{"key":"932_CR29","doi-asserted-by":"crossref","unstructured":"A. Kohlakala, J. Coetzer, J. Bertels, and D. Vandermeulen, \u201cDeep learning-based dental implant recognition using synthetic X-ray images,\u201d Medical & Biological Engineering & Computing, vol. 60, no. 10, pp. 2951-2968, 2022.","DOI":"10.1007\/s11517-022-02642-9"},{"key":"932_CR30","doi-asserted-by":"crossref","unstructured":"R. Patel, E. H. Thong, V. Batta, A. A. Bharath, D. Francis, and J. Howard, \u201cAutomated identification of orthopedic implants on radiographs using deep learning,\u201d Radiology: Artificial Intelligence, vol. 3, no. 4, 2021.4765-4774.","DOI":"10.1148\/ryai.2021200183"},{"key":"932_CR31","doi-asserted-by":"crossref","unstructured":"O. Sagi and L. Rokach, \u201cEnsemble learning: A survey,\u201d Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, 2018.","DOI":"10.1002\/widm.1249"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00932-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00932-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00932-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T15:16:40Z","timestamp":1709306200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00932-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["932"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00932-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"5 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was granted on 15 October 2019 by HRA and Health and Care Research Wales.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}