{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:57:26Z","timestamp":1779195446478,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"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":["Emerg Radiol"],"DOI":"10.1007\/s10140-024-02300-7","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T07:56:19Z","timestamp":1734335779000},"page":"97-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Diagnostic performance of neural network algorithms in skull fracture detection on CT\u00a0scans: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"32","author":[{"given":"Guive","family":"Sharifi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramtin","family":"Hajibeygi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Ali Modares","family":"Zamani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Mohamedbaqer","family":"Easa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashkan","family":"Bahrami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Eshraghi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maral","family":"Moafi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Javad","family":"Ebrahimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mobina","family":"Fathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arshia","family":"Mirjafari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janine S.","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Dixe de Oliveira Santo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahsa Asadi","family":"Anar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omidvar","family":"Rezaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long H.","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"2300_CR1","doi-asserted-by":"crossref","unstructured":"Gakuu LN (2011) The challenge of fracture management in osteoporotic bones. East Afr Orthop J 4","DOI":"10.4314\/eaoj.v4i1.63606"},{"issue":"16","key":"2300_CR2","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1024\/1661-8157\/a001022","volume":"101","author":"MJ Scheyerer","year":"2012","unstructured":"Scheyerer MJ, Simmen H-P, Wanner GA, Werner CML (2012) Osteoporotic fractures of axial skeleton. Praxis 101(16):1021\u20131030","journal-title":"Praxis"},{"key":"2300_CR3","doi-asserted-by":"crossref","unstructured":"Jacob O, Prathap A (2021) Maxillary Fractures. Oral and Maxillofacial Surgery for the Clinician","DOI":"10.1007\/978-981-15-1346-6_55"},{"key":"2300_CR4","unstructured":"Furlow B 2014 Computed tomography of facial fractures. Radiologic technology. 85 5:523CT-39CT; quiz 40CT-42CT"},{"issue":"2","key":"2300_CR5","doi-asserted-by":"crossref","first-page":"221.e1","DOI":"10.1016\/j.amjmed.2015.09.020","volume":"129","author":"DL Kendler","year":"2016","unstructured":"Kendler DL, Bauer DC, Davison KS, Dian L, Hanley DA, Harris ST et al (2016) Vertebral Fractures: Clinical Importance and Management. Am J Med 129(2):221.e1\u201310","journal-title":"Am J Med"},{"key":"2300_CR6","doi-asserted-by":"crossref","first-page":"910","DOI":"10.31616\/asj.2020.0593","volume":"14","author":"G-U Kim","year":"2020","unstructured":"Kim G-U, Chang MC, Kim TU, Lee GW (2020) Diagnostic Modality in Spine Disease: A Review. Asian Spine J 14:910\u2013920","journal-title":"Asian Spine J"},{"key":"2300_CR7","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s004020000257","volume":"121","author":"CM Bach","year":"2001","unstructured":"Bach CM, Steingruber I, Peer S, Peer-K\u00fchberger R, Jaschke W, Ogon M (2001) Radiographic evaluation of cervical spine trauma. Arch Orthop Trauma Surg 121:385\u2013387","journal-title":"Arch Orthop Trauma Surg"},{"key":"2300_CR8","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1007\/s00330-004-2631-7","volume":"15","author":"B Schuknecht","year":"2005","unstructured":"Schuknecht B, Graetz KW (2005) Radiologic assessment of maxillofacial, mandibular, and skull base trauma. Eur Radiol 15:560\u2013568","journal-title":"Eur Radiol"},{"key":"2300_CR9","unstructured":"iData Research (2021) Over 75 million CT scans are performed each year and growing despite radiation concerns.\u00a0 [cited 2024 Nov 25]. Available from: https:\/\/idataresearch.com\/over-75-million-ct-scans-are-performed-each-year-and-growing-despite-radiation-concerns\/"},{"issue":"3","key":"2300_CR10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1093\/bjs\/znaa091","volume":"108","author":"K Treskes","year":"2021","unstructured":"Treskes K, Sierink JC, Edwards MJR, Beuker BJA, Van Lieshout EMM, Hohmann J et al (2021) Cost-effectiveness of immediate total-body CT in patients with severe trauma (REACT-2 trial). Br J Surg 108(3):277\u2013285","journal-title":"Br J Surg"},{"issue":"2","key":"2300_CR11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/17453674.2019.1711323","volume":"91","author":"PHS Kalmet","year":"2020","unstructured":"Kalmet PHS, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T et al (2020) Deep learning in fracture detection: a narrative review. Acta Orthop 91(2):215\u2013220","journal-title":"Acta Orthop"},{"issue":"3","key":"2300_CR12","doi-asserted-by":"crossref","first-page":"611","DOI":"10.2214\/AJR.12.10375","volume":"201","author":"CS Lee","year":"2013","unstructured":"Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE (2013) Cognitive and system factors contributing to diagnostic errors in radiology. Am J Roentgenol 201(3):611\u2013617","journal-title":"Am J Roentgenol"},{"issue":"2","key":"2300_CR13","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1148\/radiol.12111119","volume":"266","author":"JS Whang","year":"2013","unstructured":"Whang JS, Baker SR, Patel R, Luk L, Castro A 3rd (2013) The causes of medical malpractice suits against radiologists in the United States. Radiology 266(2):548\u2013554","journal-title":"Radiology"},{"issue":"1","key":"2300_CR14","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ajem.2009.07.011","volume":"29","author":"B Petinaux","year":"2011","unstructured":"Petinaux B, Bhat R, Boniface K, Aristizabal J (2011) Accuracy of radiographic readings in the emergency department. Am J Emerg Med 29(1):18\u201325","journal-title":"Am J Emerg Med"},{"issue":"1061","key":"2300_CR15","doi-asserted-by":"crossref","first-page":"20150914","DOI":"10.1259\/bjr.20150914","volume":"89","author":"A Pinto","year":"2016","unstructured":"Pinto A, Reginelli A, Pinto F, Lo Re G, Midiri F, Muzj C et al (2016) Errors in imaging patients in the emergency setting. Br J Radiol 89(1061):20150914","journal-title":"Br J Radiol"},{"issue":"1","key":"2300_CR16","doi-asserted-by":"crossref","first-page":"e180001","DOI":"10.1148\/ryai.2019180001","volume":"1","author":"YL Thian","year":"2019","unstructured":"Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT (2019) Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs. Radiol Artif Intell 1(1):e180001","journal-title":"Radiol Artif Intell"},{"key":"2300_CR17","unstructured":"Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet V. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:13126082. 2013"},{"key":"2300_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"2300_CR19","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10278-017-9993-2","volume":"30","author":"P Teare","year":"2017","unstructured":"Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E (2017) Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement. J Digit Imaging 30(4):499\u2013505","journal-title":"J Digit Imaging"},{"issue":"2","key":"2300_CR20","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B (2017) Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology 284(2):574\u2013582","journal-title":"Radiology"},{"issue":"22","key":"2300_CR21","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A et al (2016) Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 316(22):2402\u20132410","journal-title":"JAMA"},{"issue":"1","key":"2300_CR22","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1148\/radiol.2017170236","volume":"287","author":"DB Larson","year":"2018","unstructured":"Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP (2018) Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs. Radiology 287(1):313\u2013322","journal-title":"Radiology"},{"issue":"1","key":"2300_CR23","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/1754-9485.12828","volume":"63","author":"M Adams","year":"2019","unstructured":"Adams M, Chen W, Holcdorf D, McCusker MW, Howe PD, Gaillard F (2019) Computer vs human: Deep learning versus perceptual training for the detection of neck of femur fractures. J Med Imaging Radiat Oncol 63(1):27\u201332","journal-title":"J Med Imaging Radiat Oncol"},{"issue":"4","key":"2300_CR24","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/17453674.2018.1453714","volume":"89","author":"SW Chung","year":"2018","unstructured":"Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Yoon JP et al (2018) Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 89(4):468\u2013473","journal-title":"Acta Orthop"},{"issue":"45","key":"2300_CR25","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","volume":"115","author":"R Lindsey","year":"2018","unstructured":"Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S et al (2018) Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A 115(45):11591\u201311596","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2300_CR26","doi-asserted-by":"crossref","unstructured":"Bl\u00fcthgen C, Becker AS, Vittoria de Martini I, Meier A, Martini K, Frauenfelder T (2020) Detection and localization of distal radius fractures: Deep learning system versus radiologists. Eur J Radiol. 126:108925","DOI":"10.1016\/j.ejrad.2020.108925"},{"issue":"6","key":"2300_CR27","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/17453674.2017.1344459","volume":"88","author":"J Olczak","year":"2017","unstructured":"Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A et al (2017) Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop 88(6):581\u2013586","journal-title":"Acta Orthop"},{"key":"2300_CR28","doi-asserted-by":"crossref","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj 372.","DOI":"10.1136\/bmj.n71"},{"issue":"11","key":"2300_CR29","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1002\/sim.1186","volume":"21","author":"JP Higgins","year":"2002","unstructured":"Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539\u20131558","journal-title":"Stat Med"},{"issue":"2","key":"2300_CR30","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cct.2006.04.004","volume":"28","author":"R DerSimonian","year":"2007","unstructured":"DerSimonian R, Kacker R (2007) Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 28(2):105\u2013114","journal-title":"Contemp Clin Trials"},{"issue":"7109","key":"2300_CR31","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1136\/bmj.315.7109.629","volume":"315","author":"M Egger","year":"1997","unstructured":"Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629\u2013634","journal-title":"BMJ"},{"issue":"8","key":"2300_CR32","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529\u2013536","journal-title":"Ann Intern Med"},{"issue":"6","key":"2300_CR33","doi-asserted-by":"crossref","first-page":"4593","DOI":"10.1007\/s00784-022-04427-8","volume":"26","author":"X Wang","year":"2022","unstructured":"Wang X, Xu Z, Tong Y, Xia L, Jie B, Ding P et al (2022) Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin Oral Investig 26(6):4593\u20134601","journal-title":"Clin Oral Investig"},{"key":"2300_CR34","doi-asserted-by":"crossref","unstructured":"Lee MJ, Hong H, Shim KW, Park S (2019) MGB-NET: Orbital bone segmentation from head and neck ct images using multi-graylevel-bone convolutional networks 2019. 692\u20135 p","DOI":"10.1109\/ISBI.2019.8759424"},{"issue":"2","key":"2300_CR35","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1097\/SCS.0000000000006069","volume":"31","author":"L Li","year":"2020","unstructured":"Li L, Song X, Guo Y, Liu Y, Sun R, Zou H et al (2020) Deep convolutional neural networks for automatic detection of orbital blowout fractures. J Craniofac Surg 31(2):400\u2013403","journal-title":"J Craniofac Surg"},{"key":"2300_CR36","doi-asserted-by":"crossref","first-page":"687931","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 (2021) Automated identification of skull fractures with deep learning: a comparison between object detection and segmentation approach. Front Neurol 12:687931","journal-title":"Front Neurol"},{"issue":"14","key":"2300_CR37","doi-asserted-by":"crossref","first-page":"6293","DOI":"10.3390\/app11146293","volume":"11","author":"M Amodeo","year":"2021","unstructured":"Amodeo M, Abbate V, Arpaia P, Cuocolo R, Dell\u2019AversanaOrabona G, Murero M et al (2021) Transfer learning for an automated detection system of fractures in patients with maxillofacial trauma. Appl Sci 11(14):6293","journal-title":"Appl Sci"},{"issue":"2","key":"2300_CR38","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3390\/s22020506","volume":"22","author":"YJ Seol","year":"2022","unstructured":"Seol YJ, Kim YJ, Kim YS, Cheon YW, Kim KG (2022) A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures. Sensors 22(2):506","journal-title":"Sensors"},{"key":"2300_CR39","doi-asserted-by":"crossref","unstructured":"Emon MM, Ornob TR, Rahman M (2022) editors. Predicting skull fractures via CNN with classification algorithms. Proceedings of the 2nd international conference on computing advancements","DOI":"10.1145\/3542954.3543017"},{"issue":"4","key":"2300_CR40","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1167\/tvst.12.4.7","volume":"12","author":"XL Bao","year":"2023","unstructured":"Bao XL, Zhan X, Wang L, Zhu Q, Fan B, Li GY (2023) Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence. Transl Vis Sci Technol 12(4):7","journal-title":"Transl Vis Sci Technol"},{"issue":"4","key":"2300_CR41","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1007\/s10278-023-00829-6","volume":"36","author":"HC Wang","year":"2023","unstructured":"Wang HC, Wang SC, Yan JL, Ko LW (2023) Artificial Intelligence Model Trained with Sparse Data to Detect Facial and Cranial Bone Fractures from Head CT. J Digit Imaging 36(4):1408\u20131418","journal-title":"J Digit Imaging"},{"issue":"8","key":"2300_CR42","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.joms.2023.04.013","volume":"81","author":"Y Tong","year":"2023","unstructured":"Tong Y, Jie B, Wang X, Xu Z, Ding P, He Y (2023) Is Convolutional Neural Network Accurate for Automatic Detection of Zygomatic Fractures on Computed Tomography? J Oral Maxillofac Surg 81(8):1011\u20131020","journal-title":"J Oral Maxillofac Surg"},{"key":"2300_CR43","doi-asserted-by":"crossref","first-page":"79061","DOI":"10.1109\/ACCESS.2022.3192389","volume":"10","author":"G Moon","year":"2022","unstructured":"Moon G, Kim S, Kim W, Kim Y, Jeong Y, Choi H-S (2022) Computer aided facial bone fracture diagnosis (CA-FBFD) system based on object detection model. IEEE Access 10:79061\u201379070","journal-title":"IEEE Access"},{"key":"2300_CR44","doi-asserted-by":"crossref","unstructured":"Morita D, Kawarazaki A, Soufi M, Otake Y, Sato Y, Numajiri T (2024) Automatic detection of midfacial fractures in facial bone CT images using deep learning-based object detection models. J Stomatol Oral Maxillofac Surg 101914","DOI":"10.1016\/j.jormas.2024.101914"},{"issue":"3","key":"2300_CR45","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1002\/mp.15507","volume":"49","author":"A Amjad","year":"2022","unstructured":"Amjad A, Xu J, Thill D, Lawton C, Hall W, Awan MJ et al (2022) General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis. Med Phys 49(3):1686\u20131700","journal-title":"Med Phys"},{"key":"2300_CR46","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jofri.2018.08.001","volume":"14","author":"J Heimer","year":"2018","unstructured":"Heimer J, Thali MJ, Ebert L (2018) Classification based on the presence of skull fractures on curved maximum intensity skull projections by means of deep learning. J Forensic Radiol Imag 14:16\u201320","journal-title":"J Forensic Radiol Imag"},{"key":"2300_CR47","unstructured":"You L, Zhang G, Zhao W, R MG, David L, Zhou X (2020) Automated Sagittal Craniosynostosis Classification from CT Images Using Transfer Learning. Clin Surg 5"},{"key":"2300_CR48","doi-asserted-by":"crossref","first-page":"687931","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 (2021) Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach. Front Neurol 12:687931","journal-title":"Front Neurol"},{"key":"2300_CR49","doi-asserted-by":"crossref","unstructured":"Emon MR, Ornob TR, Rahman M (2022) Predicting skull fractures via CNN with classification algorithms. Proceedings of the 2nd Int Conf Comput Adv","DOI":"10.1145\/3542954.3543017"},{"key":"2300_CR50","doi-asserted-by":"crossref","unstructured":"Lin X, Yan Z, Kuang Z, Zhang H, Deng X, Yu L (2022) Fracture R-CNN: An anchor-efficient anti-interference framework for skull fracture detection in CT images. Medical physics","DOI":"10.1002\/mp.15809"},{"key":"2300_CR51","doi-asserted-by":"crossref","unstructured":"Yang C, Yang L, Gao G-d, Zong H-Q (2022) Gao D. Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures. Technol Health Care : Off J Eur Soc Eng Med","DOI":"10.3233\/THC-220501"},{"key":"2300_CR52","doi-asserted-by":"crossref","unstructured":"Bao X-l, Zhan X, Wang L, Zhu Q, Fan B, Li G-Y (2023) Automatic Identification and Segmentation of Orbital Blowout Fractures Based on Artificial Intelligence. Translat Vision Sci Technol 12(4):7-.","DOI":"10.1167\/tvst.12.4.7"},{"issue":"1\u20133","key":"2300_CR53","first-page":"167","volume":"214","author":"MH Mulroy","year":"2012","unstructured":"Mulroy MH, Loyd AM, Frush DP, Verla TG, Myers BS, Bass CR (2012) Evaluation of pediatric skull fracture imaging techniques. Forensic Sci Int 214(1\u20133):167\u2013172","journal-title":"Forensic Sci Int"},{"issue":"3","key":"2300_CR54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1097\/PEC.0000000000001397","volume":"36","author":"J Ryu","year":"2020","unstructured":"Ryu J, Yun SJ, Lee SH, Choi YH (2020) Screening of Pediatric Facial Fractures by Brain Computed Tomography: Diagnostic Performance Comparison With Facial Computed Tomography. Pediatr Emerg Care 36(3):125\u2013129","journal-title":"Pediatr Emerg Care"},{"issue":"1","key":"2300_CR55","doi-asserted-by":"crossref","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 (1998) Head trauma: CT scan interpretation by radiology residents versus staff radiologists. Radiology 208(1):125\u2013128","journal-title":"Radiology"},{"issue":"6","key":"2300_CR56","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1002\/jbm.b.35001","volume":"110","author":"K Balasubramaniyan","year":"2022","unstructured":"Balasubramaniyan K, Bhoobalan K, Jayaraman D, Sounderraj S, Muthuukumar KR, Santhini E (2022) Development and assessment of biologically compatible anterior cruciate ligament using braided ultra-high molecular weight polyethylene. J Biomed Mater Res B Appl Biomater 110(6):1306\u20131318","journal-title":"J Biomed Mater Res B Appl Biomater"},{"issue":"3","key":"2300_CR57","doi-asserted-by":"crossref","first-page":"343","DOI":"10.3348\/kjr.2021.0449","volume":"23","author":"JW Choi","year":"2022","unstructured":"Choi JW, Cho YJ, Ha JY, Lee YY, Koh SY, Seo JY et al (2022) Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs. Korean J Radiol 23(3):343\u2013354","journal-title":"Korean J Radiol"}],"container-title":["Emergency Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10140-024-02300-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10140-024-02300-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10140-024-02300-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T21:03:22Z","timestamp":1738616602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10140-024-02300-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,16]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["2300"],"URL":"https:\/\/doi.org\/10.1007\/s10140-024-02300-7","relation":{},"ISSN":["1438-1435"],"issn-type":[{"value":"1438-1435","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,16]]},"assertion":[{"value":"17 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}