{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:24:36Z","timestamp":1770524676546,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"DOI":"10.1007\/s10278-023-00829-6","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T16:02:47Z","timestamp":1682352167000},"page":"1408-1418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-853X","authenticated-orcid":false,"given":"Huan-Chih","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shao-Chung","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiun-Lin","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Li-Wei","family":"Ko","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"829_CR1","doi-asserted-by":"crossref","unstructured":"Baugnon KL, Hudgins PA: Skull base fractures and their complications. Neuroimaging Clin N Am 24:439\u2013465, vii-viii, 2014","DOI":"10.1016\/j.nic.2014.03.001"},{"key":"829_CR2","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s10143-005-0396-3","volume":"29","author":"S Yilmazlar","year":"2006","unstructured":"Yilmazlar S, Arslan E, Kocaeli H, Dogan S, Aksoy K, Korfali E, et al: Cerebrospinal fluid leakage complicating skull base fractures: analysis of 81 cases. Neurosurg Rev 29:64-71, 2006","journal-title":"Neurosurg Rev"},{"key":"829_CR3","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1046\/j.1440-1673.2003.01204.x","volume":"47","author":"G Arendts","year":"2003","unstructured":"Arendts G, Manovel A, Chai A: Cranial CT interpretation by senior emergency department staff. Australas Radiol 47:368-374, 2003","journal-title":"Australas Radiol"},{"key":"829_CR4","doi-asserted-by":"publisher","first-page":"86","DOI":"10.5812\/traumamon.12023","volume":"18","author":"A Arhami Dolatabadi","year":"2013","unstructured":"Arhami Dolatabadi A, Baratloo A, Rouhipour A, Abdalvand A, Hatamabadi H, Forouzanfar M, et al: Interpretation of Computed Tomography of the Head: Emergency Physicians versus Radiologists. Trauma Mon 18:86-89, 2013","journal-title":"Trauma Mon"},{"key":"829_CR5","first-page":"103","volume":"23","author":"WK Erly","year":"2002","unstructured":"Erly WK, Berger WG, Krupinski E, Seeger JF, Guisto JA: Radiology resident evaluation of head CT scan orders in the emergency department. AJNR Am J Neuroradiol 23:103-107, 2002","journal-title":"AJNR Am J Neuroradiol"},{"key":"829_CR6","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1148\/radiology.208.1.9646802","volume":"208","author":"MG Wysoki","year":"1998","unstructured":"Wysoki MG, Nassar CJ, Koenigsberg RA, Novelline RA, Faro SH, Faerber EN: Head trauma: CT scan interpretation by radiology residents versus staff radiologists. Radiology 208:125-128, 1998","journal-title":"Radiology"},{"key":"829_CR7","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.jcms.2013.05.018","volume":"42","author":"U Perheentupa","year":"2014","unstructured":"Perheentupa U, Makitie AA, Karhu JO, Koivunen P, Blanco Sequieros R, Kinnunen I: Frontobasilar fractures: proposal for image reviewing algorithm. J Craniomaxillofac Surg 42:305-312, 2014","journal-title":"J Craniomaxillofac Surg"},{"key":"829_CR8","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1148\/rg.2019180118","volume":"39","author":"HR Bello","year":"2019","unstructured":"Bello HR, Graves JA, Rohatgi S, Vakil M, McCarty J, Van Hemert RL, et al: Skull Base-related Lesions at Routine Head CT from the Emergency Department: Pearls, Pitfalls, and Lessons Learned. Radiographics 39:1161-1182, 2019","journal-title":"Radiographics"},{"key":"829_CR9","doi-asserted-by":"publisher","first-page":"5781790","DOI":"10.1155\/2016\/5781790","volume":"2016","author":"K Maetani","year":"2016","unstructured":"Maetani K, Namiki J, Matsumoto S, Matsunami K, Narumi A, Tsuneyoshi T, et al: Routine Head Computed Tomography for Patients in the Emergency Room with Trauma Requires Both Thick- and Thin-Slice Images. Emerg Med Int 2016:5781790, 2016","journal-title":"Emerg Med Int"},{"key":"829_CR10","first-page":"2015","volume":"2973\u20132976","author":"SM Soroushmehr","year":"2015","unstructured":"Soroushmehr SM, Bafna A, Schlosser S, Ward K, Derksen H, Najarian K: CT image segmentation in traumatic brain injury. Annu Int Conf IEEE Eng Med Biol Soc 2015:2973-2976, 2015","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"829_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s12024-021-00431-8","volume":"18","author":"V Ibanez","year":"2022","unstructured":"Ibanez V, Gunz S, Erne S, Rawdon EJ, Ampanozi G, Franckenberg S, et al: RiFNet: Automated rib fracture detection in postmortem computed tomography. Forensic Sci Med Pathol 18:20-29, 2022","journal-title":"Forensic Sci Med Pathol"},{"key":"829_CR12","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1097\/CORR.0000000000000848","volume":"477","author":"DWG Langerhuizen","year":"2019","unstructured":"Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs G, et al: What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clin Orthop Relat Res 477:2482-2491, 2019","journal-title":"Clin Orthop Relat Res"},{"key":"829_CR13","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s00256-021-03709-8","volume":"50","author":"XH Meng","year":"2021","unstructured":"Meng XH, Wu DJ, Wang Z, Ma XL, Dong XM, Liu AE, et al: A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance. Skeletal Radiol 50:1821-1828, 2021","journal-title":"Skeletal Radiol"},{"key":"829_CR14","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","volume":"171","author":"YD Pranata","year":"2019","unstructured":"Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, et al: Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed 171:27-37, 2019","journal-title":"Comput Methods Programs Biomed"},{"key":"829_CR15","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","volume":"392","author":"S Chilamkurthy","year":"2018","unstructured":"Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al: Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392:2388-2396, 2018","journal-title":"Lancet"},{"key":"829_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.687931","volume":"12","author":"W Shan","year":"2021","unstructured":"Shan W, Guo J, Mao X, Zhang Y, Huang Y, Wang S, et al: Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach. Front Neurol 12:687931, 2021","journal-title":"Front Neurol"},{"key":"829_CR17","first-page":"2016","volume":"6437\u20136440","author":"A Yamada","year":"2016","unstructured":"Yamada A, Teramoto A, Otsuka T, Kudo K, Anno H, Fujita H: Preliminary study on the automated skull fracture detection in CT images using black-hat transform. Annu Int Conf IEEE Eng Med Biol Soc 2016:6437-6440, 2016","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"829_CR18","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1148\/rg.2015140177","volume":"35","author":"S Idriz","year":"2015","unstructured":"Idriz S, Patel JH, Ameli Renani S, Allan R, Vlahos I: CT of Normal Developmental and Variant Anatomy of the Pediatric Skull: Distinguishing Trauma from Normality. Radiographics 35:1585-1601, 2015","journal-title":"Radiographics"},{"key":"829_CR19","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM: YOLOv4: Optimal Speed and Accuracy of Object Detection, in, 2020, p arXiv:2004.10934"},{"key":"829_CR20","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, de Lange T, Halvorsen P, et al: ResUNet++: An Advanced Architecture for Medical Image Segmentation, in, 2019, p arXiv:1911.07067","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"829_CR21","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, et al: Microsoft COCO: Common Objects in Context, in, 2014, p arXiv:1405.0312","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"829_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102875","volume":"112","author":"H He","year":"2022","unstructured":"He H, Xu H, Zhang Y, Gao K, Li H, Ma L, et al: Mask R-CNN based automated identification and extraction of oil well sites. International Journal of Applied Earth Observation and Geoinformation 112:102875, 2022","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"829_CR23","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al: Ssd: Single shot multibox detector, in Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14: Springer, 2016, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"829_CR24","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A: YOLO9000: better, faster, stronger, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"829_CR25","doi-asserted-by":"publisher","first-page":"e218","DOI":"10.1002\/mp.13764","volume":"47","author":"HP Chan","year":"2020","unstructured":"Chan HP, Hadjiiski LM, Samala RK: Computer-aided diagnosis in the era of deep learning. Med Phys 47:e218-e227, 2020","journal-title":"Med Phys"},{"key":"829_CR26","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s12194-019-00552-4","volume":"13","author":"H Fujita","year":"2020","unstructured":"Fujita H: AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 13:6-19, 2020","journal-title":"Radiol Phys Technol"},{"key":"829_CR27","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1038\/s41467-021-21311-3","volume":"12","author":"CT Cheng","year":"2021","unstructured":"Cheng CT, Wang Y, Chen HW, Hsiao PM, Yeh CN, Hsieh CH, et al: A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nat Commun 12:1066, 2021","journal-title":"Nat Commun"},{"key":"829_CR28","doi-asserted-by":"crossref","unstructured":"Hardalac F, Uysal F, Peker O, Ciceklidag M, Tolunay T, Tokgoz N, et al: Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models. Sensors (Basel) 22, 2022","DOI":"10.3390\/s22031285"},{"key":"829_CR29","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.patrec.2019.06.015","volume":"125","author":"B Guan","year":"2019","unstructured":"Guan B, Yao J, Zhang G, Wang X: Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network. Pattern Recognition Letters 125:521-526, 2019","journal-title":"Pattern Recognition Letters"},{"key":"829_CR30","first-page":"756","volume":"2021","author":"G Liu","year":"2021","unstructured":"Liu G, Wu Q, Yuan G, Wu X: Skull Fracture Detection Method Based on Improved Feature Pyramid Network, in 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), 2021, pp 756-762","journal-title":"International Conference on Electronic Information Engineering and Computer Science (EIECS)"},{"key":"829_CR31","first-page":"608","volume":"6","author":"SC Patel","year":"2021","unstructured":"Patel SC: Survey on Different Object Detection and Segmentation Methods. Int J Innov Sci Technol 6:608-611, 2021","journal-title":"Int J Innov Sci Technol"},{"key":"829_CR32","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3171\/2015.3.PEDS1553","volume":"16","author":"G Orman","year":"2015","unstructured":"Orman G, Wagner MW, Seeburg D, Zamora CA, Oshmyansky A, Tekes A, et al: Pediatric skull fracture diagnosis: should 3D CT reconstructions be added as routine imaging? J Neurosurg Pediatr 16:426-431, 2015","journal-title":"J Neurosurg Pediatr"},{"key":"829_CR33","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1007\/s00330-009-1435-1","volume":"19","author":"H Ringl","year":"2009","unstructured":"Ringl H, Schernthaner R, Philipp MO, Metz-Schimmerl S, Czerny C, Weber M, et al: Three-dimensional fracture visualisation of multidetector CT of the skull base in trauma patients: comparison of three reconstruction algorithms. Eur Radiol 19:2416-2424, 2009","journal-title":"Eur Radiol"},{"key":"829_CR34","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.ijom.2020.09.002","volume":"50","author":"MF de Carvalho","year":"2021","unstructured":"de Carvalho MF, Vieira JNM, Figueiredo R, Reher P, Chrcanovic BR, Chaves M: Validity of computed tomography in diagnosing midfacial fractures. Int J Oral Maxillofac Surg 50:471-476, 2021","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"Suppl 1","key":"829_CR35","doi-asserted-by":"publisher","first-page":"S117","DOI":"10.1148\/rg.26si065502","volume":"26","author":"GM Fatterpekar","year":"2006","unstructured":"Fatterpekar GM, Doshi AH, Dugar M, Delman BN, Naidich TP, Som PM: Role of 3D CT in the evaluation of the temporal bone. Radiographics 26 Suppl 1:S117-132, 2006","journal-title":"Radiographics"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00829-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00829-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00829-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T17:12:13Z","timestamp":1691428333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00829-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["829"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00829-6","relation":{},"ISSN":["1618-727X"],"issn-type":[{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,24]]},"assertion":[{"value":"24 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tri-Service General Hospital, Taipei, Taiwan (IRB1-106\u201305-070) and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan (VGHKS18-CT7-10).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}