Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.
Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 6; pp. 2874 - 2883 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283969&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283969 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283969 182283969 182283969 10.1007/s10278-024-01144-4 182283969 ppf: 2874 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture. aug: au: Gan, Kaifeng Liu, Yunpeng Zhang, Ting Xu, Dingli Lian, Leidong Luo, Zhe Li, Jin Lu, Liangjie affil: https://ror.org/03et85d35 Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, 315211, Ningbo, Zhejiang, China sug: subj: Deep Learning Utilization Radius Fractures, Distal Classification Prediction Models Wrist Wrist Fractures Radiography Automation Radius Fractures, Distal Radiography Human ROC Curve Descriptive Statistics Confidence Intervals China Funding Source Retrospective Design Record Review ab: Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926–0.965) and 0.936 (95%CI: 0.913–0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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