{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:59:38Z","timestamp":1792000778711,"version":"4.4.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    Skull fractures caused by head trauma can lead to life-threatening complications. Hence, timely and accurate identification of fractures is of great importance. Therefore, this study aims to develop a deep learning system for automated identification of skull fractures from cranial computed tomography (CT) scans.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Method:<\/jats:bold>\n                    This study retrospectively analyzed CT scans of 4,782 patients (median age, 54 years; 2,583 males, 2,199 females; development set:\n                    <jats:italic>n<\/jats:italic>\n                    = 4,168, test set:\n                    <jats:italic>n<\/jats:italic>\n                    = 614) diagnosed with skull fractures between September 2016 and September 2020. Additional data of 7,856 healthy people were included in the analysis to reduce the probability of false detection. Skull fractures in all the scans were manually labeled by seven experienced neurologists. Two deep learning approaches were developed and tested for the identification of skull fractures. In the first approach, the fracture identification task was treated as an object detected problem, and a YOLOv3 network was trained to identify all the instances of skull fracture. In the second approach, the task was treated as a segmentation problem and a modified attention U-net was trained to segment all the voxels representing skull fracture. The developed models were tested using an external test set of 235 patients (93 with, and 142 without skull fracture).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    On the test set, the YOLOv3 achieved average fracture detection sensitivity and specificity of 80.64, and 85.92%, respectively. On the same dataset, the modified attention U-Net achieved a fracture detection sensitivity and specificity of 82.80, and 88.73%, respectively.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    Deep learning methods can identify skull fractures with good sensitivity. The segmentation approach to fracture identification may achieve better results.\n                  <\/jats:p>","DOI":"10.3389\/fneur.2021.687931","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:35:23Z","timestamp":1635467723000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach"],"prefix":"10.3389","volume":"12","author":[{"given":"Wei","family":"Shan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewei","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingye","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenzhou","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunlun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s10143-005-0396-3","article-title":"Cerebrospinal fluid leakage complicating skull base fractures: analysis of 81 cases","volume":"29","author":"Yilmazlar","year":"2006","journal-title":"Neurosurg Rev."},{"key":"B2","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.nic.2014.03.001","article-title":"Skull base fractures and their complications","volume":"24","author":"Baugnon","year":"2014","journal-title":"Neuroimaging Clin N Am."},{"key":"B3","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1111\/j.1552-6569.2011.00688.x","article-title":"The unique features of traumatic brain injury in children. review of the characteristics of the pediatric skull and brain, mechanisms of trauma, patterns of injury, complications and their imaging findings\u2013part 1","volume":"22","author":"Pinto","year":"2012","journal-title":"J Neuroimaging."},{"key":"B4","doi-asserted-by":"publisher","first-page":"e18","DOI":"10.1111\/j.1552-6569.2011.00690.x","article-title":"The unique features of traumatic brain injury in children. review of the characteristics of the pediatric skull and brain, mechanisms of trauma, patterns of injury, complications, and their imaging findings\u2013part 2","volume":"22","author":"Pinto","year":"2012","journal-title":"J Neuroimaging."},{"key":"B5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1067\/mem.2001.109440","article-title":"Pediatric minor head trauma","volume":"37","author":"Schutzman","year":"2001","journal-title":"Ann Emerg Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3171\/2015.3.PEDS1553","article-title":"Pediatric skull fracture diagnosis: should 3D CT reconstructions be added as routine imaging?","volume":"16","author":"Orman","year":"2015","journal-title":"J Neurosurg Pediatr."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/ICCV.2015.123","article-title":"Delving deep into rectifiers: surpassing human-level performance on ImageNet classification","author":"He","year":"2015","journal-title":"2015 IEEE International Conference on Computer Vision (ICCV)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1166\/jmihi.2018.2410","article-title":"Basic study on the automated detection method of skull fracture in head ct images using surface selective black-hat transform","volume":"8","author":"Yamada","year":"2018","journal-title":"J Med Imaging Health Inform."},{"key":"B9","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","article-title":"Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study","volume":"392","author":"Chilamkurthy","year":"2018","journal-title":"Lancet."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/ISKE.2008.4731105","article-title":"Automated method of fracture detection in CT brain images","author":"Zaki","year":"2008","journal-title":"2008 3rd International Conference on Intelligent System and Knowledge Engineering"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","author":"Russakovsky","year":"2014","journal-title":"Int J Computer Vision."},{"key":"B12","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA."},{"key":"B13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med Image Anal."},{"key":"B14","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.2147\/OTT.S80733","article-title":"Computer-aided classification of lung nodules on computed tomography images via deep learning technique","volume":"8","author":"Hua","year":"2015","journal-title":"Onco Targets Ther."},{"key":"B15","doi-asserted-by":"publisher","first-page":"e0200721","DOI":"10.1371\/journal.pone.0200721","article-title":"Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning","volume":"13","author":"Nishio","year":"2018","journal-title":"PLoS One."},{"key":"B16","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology."},{"key":"B17","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.gie.2018.01.029","article-title":"Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective","volume":"88","author":"Wang","year":"2018","journal-title":"Gastrointest Endosc."},{"key":"B18","doi-asserted-by":"publisher","first-page":"556","DOI":"10.3390\/s18020556","article-title":"Skin lesion analysis towards melanoma detection using deep learning network","volume":"18","author":"Li","year":"2018","journal-title":"Sensors."},{"key":"B19","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/bjd.16924","article-title":"Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis","volume":"180","author":"Fujisawa","year":"2019","journal-title":"Br J Dermatol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1111\/exd.13777","article-title":"Multimodal skin lesion classification using deep learning","volume":"27","author":"Yap","year":"2018","journal-title":"Exp Dermatol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature."},{"key":"B22","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.crad.2017.11.015","article-title":"Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks","volume":"73","author":"Kim","year":"2018","journal-title":"Clin Radiol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s00256-018-3016-3","article-title":"Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network","volume":"48","author":"Urakawa","year":"2019","journal-title":"Skeletal Radiol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1080\/17453674.2019.1600125","article-title":"Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments","volume":"90","author":"Gan","year":"2019","journal-title":"Acta Orthop."},{"key":"B25","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","article-title":"Automated detection and classification of the proximal humerus fracture by using deep learning algorithm","volume":"89","author":"Chung","year":"2018","journal-title":"Acta Orthop."},{"key":"B26","first-page":"382","article-title":"Skull R-CNN: a CNN-based network for the skull fracture detection","volume-title":"Medical Imaging With Deep Learning","author":"Kuang","year":"2020"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.687931\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:35:25Z","timestamp":1635467725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.687931\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":26,"alternative-id":["10.3389\/fneur.2021.687931"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2021.687931","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]},"article-number":"687931"}}