{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T11:58:08Z","timestamp":1770551888107,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper reported a study on the 3-dimensional deep-learning-based automatic diagnosis of nasal fractures. (1) Background: The nasal bone is the most protuberant feature of the face; therefore, it is highly vulnerable to facial trauma and its fractures are known as the most common facial fractures worldwide. In addition, its adhesion causes rapid deformation, so a clear diagnosis is needed early after fracture onset. (2) Methods: The collected computed tomography images were reconstructed to isotropic voxel data including the whole region of the nasal bone, which are represented in a fixed cubic volume. The configured 3-dimensional input data were then automatically classified by the deep learning of residual neural networks (3D-ResNet34 and ResNet50) with the spatial context information using a single network, whose performance was evaluated by 5-fold cross-validation. (3) Results: The classification of nasal fractures with simple 3D-ResNet34 and ResNet50 networks achieved areas under the receiver operating characteristic curve of 94.5% and 93.4% for binary classification, respectively, both indicating unprecedented high performance in the task. (4) Conclusions: In this paper, it is presented the possibility of automatic nasal bone fracture diagnosis using a 3-dimensional Resnet-based single classification network and it will improve the diagnostic environment with future research.<\/jats:p>","DOI":"10.3390\/s22020506","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4930-3913","authenticated-orcid":false,"given":"Yu Jin","family":"Seol","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University, 191, Hambangmoe-ro, Yeonsu-gu, Incheon 21936, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University College of Medicine, 38-13 Docjeom-ro 3 beon-gil, Namdong-gu, Incheon 21565, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoon Sang","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Gachon University Gil Medical Center, College of Medicine, Incheon 21565, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young Woo","family":"Cheon","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Gachon University Gil Medical Center, College of Medicine, Incheon 21565, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University College of Medicine, 38-13 Docjeom-ro 3 beon-gil, Namdong-gu, Incheon 21565, Korea"},{"name":"Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Seongnam-si 13120, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1097\/00001665-200603000-00010","article-title":"Analysis of Nasal Bone Fractures; A Six-year Study of 503 Patients","volume":"17","author":"Hwang","year":"2006","journal-title":"J. Craniofacial Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/S0266-4356(03)00165-7","article-title":"Analysis of the pattern of maxillofacial fractures in Kaduna, Nigeria","volume":"41","author":"Adebayo","year":"2003","journal-title":"Br. J. Oral Maxillofac. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1097\/00006534-200008000-00003","article-title":"Nasal Fracture Management: Minimizing Secondary Nasal Deformities","volume":"106","author":"Rohrich","year":"2000","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.bjps.2008.10.006","article-title":"Reconstruction of traumatic nasal deformity in Orientals","volume":"63","author":"Chen","year":"2010","journal-title":"J. Plast. Reconstr. Aesthet. Surg."},{"key":"ref_5","first-page":"9","article-title":"Epidemiology of Nasal Bone Fractures","volume":"3","author":"Dong","year":"2021","journal-title":"Fac. Plast. Surg. Aesthet. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"377","DOI":"10.4329\/wjr.v2.i10.377","article-title":"Spectrum of diagnostic errors in radiology","volume":"2","author":"Pinto","year":"2010","journal-title":"World J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1148\/radiol.2312030767","article-title":"Diagnostic CT scans: Assessment of patient, physician, and radiologist awareness of radiation dose and possible risks","volume":"231","author":"Lee","year":"2004","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-019-0105-1","article-title":"Deep learning predicts hip fracture using confounding patient and healthcare variables","volume":"2","author":"Badgeley","year":"2019","journal-title":"NPJ Dig. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1002\/jbmr.4146","article-title":"Clinical Utility of Computer-Aided Diagnosis of Vertebral Fractures from Computed Tomography Images","volume":"35","author":"Kolanu","year":"2020","journal-title":"J. Bone Miner. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"key":"ref_13","first-page":"409","article-title":"Lung cancer detection and classification with 3D convolutional neural network (3D-CNN)","volume":"8","author":"Alakwaa","year":"2017","journal-title":"Lung Cancer"},{"key":"ref_14","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1007\/s12555-018-0182-y","article-title":"View-point invariant 3d classification for mobile robots using a convolutional neural network","volume":"16","author":"Moon","year":"2018","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., and Guibas, L.J. (2016, January 27\u201330). Volumetric and multi-view cnns for object classification on 3d data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., and Mei, T. (2017, January 22\u201329). Learning spatio-temporal representation with pseudo-3d residual networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.590"},{"key":"ref_18","unstructured":"Murphy, M.A. (2021, June 23). Windowing (CT). Available online: https:\/\/radiopaedia.org\/articles\/windowing-ct?lang=us."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1148\/radiol.2018181432","article-title":"Automated abdominal segmentation of CT scans for body composition analysis using deep learning","volume":"290","author":"Weston","year":"2019","journal-title":"Radiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s40747-020-00199-4","article-title":"Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans","volume":"7","author":"Karar","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"58006","DOI":"10.1109\/ACCESS.2020.2981337","article-title":"Optimal feature selection-based medical image classification using deep learning model in internet of medical things","volume":"8","author":"Raj","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"e190023","article-title":"Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning","volume":"2","author":"Krogue","year":"2020","journal-title":"Radiology"},{"key":"ref_24","doi-asserted-by":"crossref","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":"ref_25","doi-asserted-by":"crossref","first-page":"103106","DOI":"10.1016\/j.ebiom.2020.103106","article-title":"Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet","volume":"62","author":"Jin","year":"2020","journal-title":"EBioMedicine"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23626","DOI":"10.1109\/ACCESS.2017.2762703","article-title":"3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI","volume":"5","author":"Zou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14290","DOI":"10.1109\/JSEN.2020.3023471","article-title":"Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors","volume":"21","author":"Singh","year":"2020","journal-title":"IEEE Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:14:46Z","timestamp":1760364886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020506"],"URL":"https:\/\/doi.org\/10.3390\/s22020506","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}