A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures
DC Field | Value | Language |
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dc.contributor.author | Seol, Yu Jin | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Kim, Yoon Sang | - |
dc.contributor.author | Cheon, Young Woo | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.date.accessioned | 2022-02-05T02:40:22Z | - |
dc.date.available | 2022-02-05T02:40:22Z | - |
dc.date.created | 2022-01-19 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83435 | - |
dc.description.abstract | 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Sensors | - |
dc.title | A Study on 3D Deep Learning-Based Automatic Diagnosis of Nasal Fractures | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000747789700001 | - |
dc.identifier.doi | 10.3390/s22020506 | - |
dc.identifier.bibliographicCitation | Sensors, v.22, no.2 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85122407565 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 22 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Seol, Yu Jin | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.contributor.affiliatedAuthor | Kim, Yoon Sang | - |
dc.contributor.affiliatedAuthor | Cheon, Young Woo | - |
dc.contributor.affiliatedAuthor | Kim, Kwang Gi | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | 3D-classification | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Computed aided diagnosis (CAD) | - |
dc.subject.keywordAuthor | Nasal fractures | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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