{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:02:52Z","timestamp":1790787772736,"version":"4.1.0"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["13GW0388A"],"award-info":[{"award-number":["13GW0388A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.<\/jats:p>","DOI":"10.3390\/diagnostics11112131","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T09:16:11Z","timestamp":1637140571000},"page":"2131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2506-4583","authenticated-orcid":false,"given":"Alena-K.","family":"Golla","sequence":"first","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9323-5559","authenticated-orcid":false,"given":"Christian","family":"T\u00f6nnes","sequence":"additional","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-3508","authenticated-orcid":false,"given":"Tom","family":"Russ","sequence":"additional","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8781-2882","authenticated-orcid":false,"given":"Dominik F.","family":"Bauer","sequence":"additional","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8501-2147","authenticated-orcid":false,"given":"Matthias F.","family":"Froelich","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen J.","family":"Diehl","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan O.","family":"Schoenberg","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Keese","sequence":"additional","affiliation":[{"name":"Department of Surgery, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lothar R.","family":"Schad","sequence":"additional","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-1394","authenticated-orcid":false,"given":"Frank G.","family":"Z\u00f6llner","sequence":"additional","affiliation":[{"name":"Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7069-5181","authenticated-orcid":false,"given":"Johann S.","family":"Rink","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/nrcardio.2010.180","article-title":"Pathophysiology and epidemiology of abdominal aortic aneurysms","volume":"8","author":"Nordon","year":"2011","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jvs.2017.10.044","article-title":"The Society for Vascular Surgery practice guidelines on the care of patients with an abdominal aortic aneurysm","volume":"67","author":"Chaikof","year":"2018","journal-title":"J. Vasc. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1053\/ejvs.1999.0974","article-title":"Ruptured Abdominal Aortic Aneurysm: A Novel Method of Outcome Prediction Using Neural Network Technology","volume":"19","author":"Turton","year":"2000","journal-title":"Eur. J. Vasc. Endovasc. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00423-016-1401-8","article-title":"Ruptured abdominal aortic aneurysm\u2014epidemiology, predisposing factors, and biology","volume":"401","author":"Keese","year":"2016","journal-title":"Langenbeck\u2019s Arch. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s00423-016-1405-4","article-title":"Current treatment strategies for ruptured abdominal aortic aneurysm","volume":"401","author":"Peters","year":"2016","journal-title":"Langenbeck\u2019s Arch. Surg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111","DOI":"10.2147\/CLEP.S31098","article-title":"Prognosis of ruptured abdominal aortic aneurysms in Denmark from 1994\u20132008","volume":"4","author":"Lindholt","year":"2012","journal-title":"Clin. Epidemiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1016\/j.jvs.2016.11.044","article-title":"Measuring abdominal aortic diameters in routine abdominal computed tomography scans and implications for abdominal aortic aneurysm screening","volume":"65","author":"Claridge","year":"2017","journal-title":"J. Vasc. Surg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1161\/CIRCULATIONAHA.118.036966","article-title":"Safety of Men With Small and Medium Abdominal Aortic Aneurysms Under Surveillance in the NAAASP","volume":"139","author":"Sweeting","year":"2019","journal-title":"Circulation"},{"key":"ref_9","first-page":"2","article-title":"Comparison of outcomes in elective endovascular aortic repair vs open surgical repair of abdominal aortic aneurysms","volume":"7","author":"Salata","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on mri","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s13239-019-00421-6","article-title":"Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (aaa) using convolutional neural networks and hough circles algorithm","volume":"10","author":"Mohammadi","year":"2019","journal-title":"Cardiovasc. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.media.2018.03.010","article-title":"Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative cta images using deep convolutional neural networks","volume":"46","author":"Aranjuelo","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Habijan, M., Gali\u0107, I., Leventi\u0107, H., Romi\u0107, K., and Babin, D. (2020, January 14\u201315). Abdominal aortic aneurysm segmentation from ct images using modified 3d u-net with deep supervision. Proceedings of the IEEE 2020 International Symposium ELMAR, Zadar, Croatia.","DOI":"10.1109\/ELMAR49956.2020.9219015"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/JBHI.2019.2896034","article-title":"Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration","volume":"23","author":"Zhang","year":"2019","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TBME.2018.2852306","article-title":"Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface","volume":"66","author":"Do","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10278-011-9417-7","article-title":"Evaluation of Texture for Classification of Abdominal Aortic Aneurysm after Endovascular Repair","volume":"25","author":"Maiora","year":"2012","journal-title":"J. Digit. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1007\/s10278-019-00281-5","article-title":"Classification of aortic dissection and rupture on post-contrast ct images using a convolutional neural network","volume":"32","author":"Harris","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108713","DOI":"10.1016\/j.ejrad.2019.108713","article-title":"Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning","volume":"121","author":"Cao","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e202","DOI":"10.1016\/j.jvs.2019.04.291","article-title":"SS26. Machine Deep Learning Accurately Detects Endoleak After Endovascular Abdominal Aortic Aneurysm Repair","volume":"69","author":"Hahn","year":"2019","journal-title":"J. Vasc. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hong, H.A., and Sheikh, U.U. (2016, January 4\u20136). Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning. Proceedings of the 2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), Melaka, Malaysia.","DOI":"10.1109\/CSPA.2016.7515839"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Corchado, E., Sn\u00e1\u0161el, V., Abraham, A., Wo\u017aniak, M., Gra\u00f1a, M., and Cho, S.B. (2012). A Hybrid Segmentation of Abdominal CT Images. Hybrid Artificial Intelligent Systems, Springer.","DOI":"10.1007\/978-3-642-28931-6"},{"key":"ref_22","unstructured":"Roth, H., Farag, A., Turkbey, E.B., Lu, L., Liu, J., and Summers, R.M. (2016). Data From Pancreas-CT. The Cancer Imaging Archive, The University of Arkansas for Medical Sciences. Available online: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/Pancreas-CT#225140400c26eab54502412cbbd0e1c0fddd917b."},{"key":"ref_23","unstructured":"Soler, L., Hostettler, A., Agnus, V., Charnoz, A., Fasquel, J.B., Moreau, J., Osswald, A.B., Bouhadjar, M., and Marescaux, J. (2010). 3D Image Reconstruction for Comparison of Algorithm Database: A Patient Specific Anatomical and Medical Image Database, Les H\u00f4pitaux Universitaires. Available online: https:\/\/www.ircad.fr\/fr\/recherche\/3d-ircadb-01-fr\/."},{"key":"ref_24","unstructured":"Kavur, E., Selver, M.A., Dicle, O., Bar\u0131\u015f, M., and Gezer, N.S. (2019). CHAOS-Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data Set], Zenodo, European Organization for Nuclear Research and OpenAIRE. Available online: http:\/\/doi.org\/10.5281\/zenodo.3362844."},{"key":"ref_25","unstructured":"Landman, A., Xu, Z., Igelsias, J.E., Styner, M., Langerak, T.R., and Klein, A. (2015). MICCAI Multi-Atlas Labeling beyond the Cranial Vault-workshop and Challenge, Synapse, Sage Bionetworks. Available online: https:\/\/doi.org\/10.7303\/syn3193805."},{"key":"ref_26","first-page":"1097","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Proceedings of the NIPS 2012"},{"key":"ref_27","unstructured":"Bengio, Y., and LeCun, Y. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. Available online: https:\/\/dblp.org\/db\/conf\/iclr\/iclr2015.html."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (, January June). Deep residual learning for image recognition. Proceedings of the CVPR 2016, Los Alamitos, CA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.-R., and Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref_30","unstructured":"Chlebus, G., Abolmaali, N., Schenk, A., and Meine, H. (2019, January 8\u201310). Relevance analysis of mri sequences for automatic liver tumor segmentation. Proceedings of the MIDL 2019, London, UK."},{"key":"ref_31","unstructured":"Schnurr, A.-K., Sch\u00f6ben, M., Hermann, I., Schmidt, R., Chlebus, G., Schad, L.R., and Z\u00f6llner, F.G. Relevance analysis of mri sequences for ms lesion detection. Proceedings of the 2020 ESMRMB Congress, Online."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process."},{"key":"ref_33","first-page":"55","article-title":"A technique for the measurement of attitudes","volume":"22","author":"Likert","year":"1932","journal-title":"Arch. Psychol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15579883211001204","DOI":"10.1177\/15579883211001204","article-title":"Screening older adult men for abdominal aortic aneurysm: A. scoping review","volume":"15","author":"Bains","year":"2021","journal-title":"Am. J. Mens Health"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.jval.2020.10.015","article-title":"Evaluating the cost-effectiveness of changes to the surveillance intervals in the uk abdominal aortic aneurysm screening programme","volume":"24","author":"Sweeting","year":"2021","journal-title":"Value Health"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yasaka, K., and Abe, O. (2018). Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002707"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","article-title":"The present and future of deep learning in radiology","volume":"114","author":"Saba","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/TPAMI.2017.2782687","article-title":"Multi-scale deep reinforcement learning for real-time 3dlandmark detection in ct scans","volume":"41","author":"Ghesu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109816","DOI":"10.1016\/j.ejrad.2021.109816","article-title":"Deep Learning-based Automated Detection of Pulmonary Embolism on CT Pulmonary Angiograms: No Significant Effects on Report Communication Times and Patient Turnaround in the Emergency Department Nine Months after Technical Implementation","volume":"141","author":"Schmuelling","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.jvs.2019.12.026","article-title":"Artificial intelligence in abdominal aortic aneurysm","volume":"72","author":"Raffort","year":"2020","journal-title":"J. Vasc. Surg."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13750","DOI":"10.1038\/s41598-019-50251-8","article-title":"A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation","volume":"9","author":"Lareyre","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1097\/RCT.0000000000000958","article-title":"Machine learning to predict the rapid growth of small abdominal aortic aneurysm","volume":"44","author":"Hirata","year":"2020","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Polat, H., and Danaei Mehr, H. (2019). Classification of pulmonary ct images by using hybrid 3d-deep convolutional neural network architecture. Appl. Sci., 9.","DOI":"10.3390\/app9050940"},{"key":"ref_44","first-page":"1","article-title":"Classification of covid-19 patients from chest ct images using multi-objective differential evolution\u2013based convolutional neural networks","volume":"39","author":"Singh","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TBME.2020.3042640","article-title":"Convolutional neural network ensemble segmentation with ratio-based sampling for the arteries and veins in abdominal ct scans","volume":"68","author":"Golla","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the ICML 2015, Lille, France."},{"key":"ref_47","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2021, October 21). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: Tensorflow.Org."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fninf.2013.00045","article-title":"The design of simpleitk","volume":"7","author":"Lowekamp","year":"2013","journal-title":"Front. Neuroinform."},{"key":"ref_49","first-page":"1","article-title":"Innvestigate neural networks!","volume":"20","author":"Alber","year":"2019","journal-title":"J. Mach. Learn. Res."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/2131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:35Z","timestamp":1760167895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/2131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,17]]},"references-count":49,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["diagnostics11112131"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11112131","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,17]]}}}