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Deep Learning Model for Automatic Identification and Classification of Distal Radius Fracture.
Gan, Kaifeng; Liu, Yunpeng; Zhang, Ting; Xu, Dingli; Lian, Leidong; Luo, Zhe; Li, Jin; Lu, Liangjie.
Afiliación
  • Gan K; Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
  • Liu Y; Ningbo University of Technology, Ningbo, 315100, Zhejiang, China.
  • Zhang T; Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
  • Xu D; Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China.
  • Lian L; Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China.
  • Luo Z; Health Science Center, Ningbo University, Ningbo, 315000, Zhejiang, China.
  • Li J; Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China.
  • Lu L; Department of Orthopaedics, the Affiliated LiHuiLi Hospital of Ningbo University, No. 57 Xingning Road, Yinzhou District, Ningbo, 315211, Zhejiang, China. lliangjiedoc@outlook.com.
J Imaging Inform Med ; 2024 Jun 11.
Article en En | MEDLINE | ID: mdl-38862852
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Imaging Inform Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Imaging Inform Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza