{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:05:07Z","timestamp":1790233507801,"version":"4.1.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.ejrad.2026.113059","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:35:10Z","timestamp":1783524910000},"page":"113059","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning for subtype classification of inner ear malformations on temporal bone HRCT: Development and multicenter validation"],"prefix":"10.1016","volume":"204","author":[{"given":"Xiaoge","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianyu","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuping","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dance","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuquan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhecheng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pu","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongyi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.ejrad.2026.113059_b0005","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/pr.2016.250","article-title":"Universal newborn hearing screening: methods and results, obstacles, and benefits","volume":"81","author":"Wroblewska-Seniuk","year":"2017","journal-title":"Pediatr. Res."},{"issue":"5","key":"10.1016\/j.ejrad.2026.113059_b0010","doi-asserted-by":"crossref","first-page":"397","DOI":"10.4274\/balkanmedj.2017.0367","article-title":"Classification and Current Management of Inner Ear Malformations","volume":"34","author":"Sennaro\u011flu","year":"2017","journal-title":"Balkan Med. J."},{"key":"10.1016\/j.ejrad.2026.113059_b0015","doi-asserted-by":"crossref","unstructured":"Sennaroglu L, editor. Inner ear malformations: classification, evaluation and treatment. Springer Nature; 2022 Jun 10.","DOI":"10.1007\/978-3-030-83674-0"},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0020","first-page":"45","article-title":"Study on 2,747 cases of inner ear malformation for its classification in patient with sensorineural hearing loss","volume":"29","author":"Sun","year":"2015","journal-title":"Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi"},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0025","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1097\/MAO.0b013e318206fc1e","article-title":"Auditory brainstem implantation in children and non-neurofibromatosis type 2 patients: a consensus statement","volume":"32","author":"Sennaroglu","year":"2011","journal-title":"Otol. Neurotol."},{"key":"10.1016\/j.ejrad.2026.113059_b0030","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.heares.2017.10.005","article-title":"An overview of cochlear implant electrode array designs","volume":"356","author":"Dhanasingh","year":"2017","journal-title":"Hear. Res."},{"key":"10.1016\/j.ejrad.2026.113059_b0035","doi-asserted-by":"crossref","unstructured":"Jackler RK, Luxford WM, House WF, Congenital malformations of the inner ear: a classification based on embryogenesis. Laryngoscope. 1987 Mar;97(3 Pt 2 Suppl 40):2-14.","DOI":"10.1002\/lary.5540971301"},{"issue":"12","key":"10.1016\/j.ejrad.2026.113059_b0040","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1097\/00005537-200212000-00019","article-title":"A new classification for cochleovestibular malformations","volume":"112","author":"Sennaroglu","year":"2002","journal-title":"Laryngoscope"},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0045","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1159\/000365584","article-title":"A new classification of cochleovestibular malformations and implications for predicting speech perception ability after cochlear implantation","volume":"20","author":"Jeong","year":"2015","journal-title":"Audiol. Neurootol."},{"issue":"5","key":"10.1016\/j.ejrad.2026.113059_b0050","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1017\/S0022215119000884","article-title":"New SMS classification of cochleovestibular malformation and its impact on decision-making","volume":"133","author":"Grover","year":"2019","journal-title":"J. Laryngol. Otol."},{"key":"10.1016\/j.ejrad.2026.113059_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108168","article-title":"A novel radiological software prototype for automatically detecting the inner ear and classifying normal from malformed anatomy","volume":"171","author":"Alkojak Almansi","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejrad.2026.113059_b0060","doi-asserted-by":"crossref","first-page":"102343","DOI":"10.1016\/j.compmedimag.2024.102343","article-title":"Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images","volume":"113","author":"L\u00f3pez Diez","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.ejrad.2026.113059_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105683","article-title":"Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images","volume":"147","author":"Ogawa","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.ejrad.2026.113059_b0070","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11604-023-01494-z","article-title":"Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT","volume":"42","author":"Li","year":"2024","journal-title":"Jpn. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113059_b0075","first-page":"652","article-title":"Unsupervised classification of congenital inner ear malformations using DeepDiffusion for latent space representation, MICCAI 2023","volume":"14224","author":"L\u00f3pez Diez","year":"2023","journal-title":"LNCS"},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0080","doi-asserted-by":"crossref","first-page":"77","DOI":"10.5152\/iao.2019.6246","article-title":"Human inner-ear malformation types captured in 3D","volume":"15","author":"Dhanasingh","year":"2019","journal-title":"J. Int. Adv. Otol."},{"issue":"18","key":"10.1016\/j.ejrad.2026.113059_b0085","doi-asserted-by":"crossref","first-page":"5097","DOI":"10.3390\/s20185097","article-title":"3D deep learning on medical images: a review","volume":"20","author":"Singh","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.ejrad.2026.113059_b0090","first-page":"703","article-title":"Accurate detection of inner ears in head CTs using a deep volume-to-volume regression network with false positive suppression and a shape-based constraint","volume":"11073","author":"Zhang","year":"2018","journal-title":"Med Image Comput Comput Assist Interv."},{"key":"10.1016\/j.ejrad.2026.113059_b0095","doi-asserted-by":"crossref","unstructured":"A.T. Radutoiu, F. Patou, J. Margeta, R.R. Paulsen, P. L\u00f3pez Diez, Accurate localization of inner ear regions of interests using deep reinforcement learning. In International Workshop on Machine Learning in Medical Imaging. (2022) 416\u2013424.","DOI":"10.1007\/978-3-031-21014-3_43"},{"key":"10.1016\/j.ejrad.2026.113059_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105387","article-title":"Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images","volume":"191","author":"Heutink","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0105","doi-asserted-by":"crossref","first-page":"e2229","DOI":"10.1002\/rcs.2229","article-title":"Automatic segmentation of temporal bone structures from clinical conventional CT using a CNN approach","volume":"17","author":"Lv","year":"2021","journal-title":"Int. J. Med. Robot."},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0110","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1186\/s12880-021-00698-x","article-title":"Fully automated segmentation in temporal bone CT with neural network: a preliminary assessment study","volume":"21","author":"Wang","year":"2021","journal-title":"BMC Med. Imaging"},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0115","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.anl.2022.06.008","article-title":"Application of UNETR for automatic cochlear segmentation in temporal bone CTs","volume":"50","author":"Li","year":"2023","journal-title":"Auris Nasus Larynx"},{"issue":"3","key":"10.1016\/j.ejrad.2026.113059_b0120","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1177\/02841851241307333","article-title":"Utilizing deep learning for automatic segmentation of the cochleae in temporal bone computed tomography","volume":"66","author":"Li","year":"2025","journal-title":"Acta Radiol."},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0125","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1186\/s41747-024-00508-3","article-title":"Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT","volume":"8","author":"Lim","year":"2024","journal-title":"Eur Radiol Exp."},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0130","doi-asserted-by":"crossref","first-page":"19057","DOI":"10.1038\/s41598-023-45466-9","article-title":"Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework","volume":"13","author":"Stebani","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.113059_b0135","first-page":"2136","article-title":"TransMIL: transformer based correlated multiple instance learning for whole slide image classification","volume":"34","author":"Shao","year":"2021","journal-title":"Adv. Neural Inform. Process. Syst. (NeurIPS)."},{"issue":"11","key":"10.1016\/j.ejrad.2026.113059_b0140","doi-asserted-by":"crossref","first-page":"E696","DOI":"10.1002\/lary.28596","article-title":"A novel classification: anomalous routes of the facial nerve in relation to inner ear malformations","volume":"130","author":"Sennaro\u011flu","year":"2020","journal-title":"Laryngoscope"},{"issue":"4","key":"10.1016\/j.ejrad.2026.113059_b0145","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1080\/14670100.2024.2413273","article-title":"Current status of pediatric auditory brainstem implantation in inner ear malformations; consensus statement of the Third International Pediatric ABI meeting","volume":"25","author":"Sennaroglu","year":"2024","journal-title":"Cochlear Implants Int."},{"issue":"11","key":"10.1016\/j.ejrad.2026.113059_b0150","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1001\/jamaoto.2025.2866","article-title":"Reevaluation of Enlarged Vestibular Aqueduct","volume":"151","author":"Huang","year":"2025","journal-title":"JAMA Otolaryngol. Head Neck Surg."},{"key":"10.1016\/j.ejrad.2026.113059_b0155","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J, YOLOX: Exceeding YOLO Series in 2021. arXiv preprint arXiv:2107.08430."},{"key":"10.1016\/j.ejrad.2026.113059_b0160","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). (2016) 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.ejrad.2026.113059_b0165","doi-asserted-by":"crossref","unstructured":"K. Hara, H. Kataoka, Y. Satoh, Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. (2018) 6546-6555.","DOI":"10.1109\/CVPR.2018.00685"},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0170","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ejrad.2026.113059_b0175","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"8","key":"10.1016\/j.ejrad.2026.113059_b0180","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.ejrad.2026.113059_b0185","doi-asserted-by":"crossref","unstructured":"T. Tong, R. Wolz, Q. Gao, et al. Multiple instance learning for classification of dementia in brain MRI, Med. Image Anal. 18 (5) (2014 Jul) 808\u2013818.","DOI":"10.1016\/j.media.2014.04.006"},{"issue":"1","key":"10.1016\/j.ejrad.2026.113059_b0190","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1186\/s13244-025-02156-6","article-title":"Integrating CT-based radiomics and deep learning for invasive prediction of ground-glass nodules in lung adenocarcinoma: a multicohort study","volume":"16","author":"Du","year":"2025","journal-title":"Insights Imaging."},{"issue":"12","key":"10.1016\/j.ejrad.2026.113059_b0195","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14547","article-title":"Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI","volume":"25","author":"Jin","year":"2024","journal-title":"J. Appl. Clin. Med. Phys."},{"issue":"6","key":"10.1016\/j.ejrad.2026.113059_b0200","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/jimaging6060052","article-title":"Explainable deep learning models in medical image analysis","volume":"6","author":"Singh","year":"2020","journal-title":"J. Imaging."},{"key":"10.1016\/j.ejrad.2026.113059_b0205","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","article-title":"Unveiling the black box: a systematic review of Explainable Artificial Intelligence in medical image analysis","volume":"24","author":"Muhammad","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"2","key":"10.1016\/j.ejrad.2026.113059_b0210","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3174\/ajnr.A2498","article-title":"Pediatric sensorineural hearing loss, Part 1: practical aspects for neuroradiologists","volume":"33","author":"Huang","year":"2012","journal-title":"AJNR Am. J. Neuroradiol."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26004079?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26004079?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:39:24Z","timestamp":1790231964000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26004079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":42,"alternative-id":["S0720048X26004079"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113059","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning for subtype classification of inner ear malformations on temporal bone HRCT: Development and multicenter validation","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113059","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113059"}}