Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy Comparable to Hand Orthopedic Surgeons.
In recent years, fracture image diagnosis using a convolutional neural network (CNN) has been reported. The purpose of the present study was to evaluate the ability of CNN to diagnose distal radius fractures (DRFs) using frontal and lateral wrist radiographs. We included 503 cases of DRF diagnosed b...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 1; pp. 39 - 47 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
|
| 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=155312938&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155312938 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2022 vid: 35 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155312938 154146104 155312938 155312938 10.1007/s10278-021-00519-1 155312938 ppf: 39 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy Comparable to Hand Orthopedic Surgeons. aug: au: Suzuki, Takeshi Maki, Satoshi Yamazaki, Takahiro Wakita, Hiromasa Toguchi, Yasunari Horii, Manato Yamauchi, Tomonori Kawamura, Koui Aramomi, Masaaki Sugiyama, Hiroshi Matsuura, Yusuke Yamashita, Takeshi Orita, Sumihisa Ohtori, Seiji affil: Department of Orthopedic Surgery, Tonosho Hospital, Chiba, Japan sug: subj: Radius Fractures, Distal Radiography Wrist Radiography Neural Networks (Computer) Orthopedic Surgery Human Deep Learning Sensitivity and Specificity ROC Curve Surgeons Hand Surgery ab: In recent years, fracture image diagnosis using a convolutional neural network (CNN) has been reported. The purpose of the present study was to evaluate the ability of CNN to diagnose distal radius fractures (DRFs) using frontal and lateral wrist radiographs. We included 503 cases of DRF diagnosed by plain radiographs and 289 cases without fracture. We implemented the CNN model using Keras and Tensorflow. Frontal and lateral views of wrist radiographs were manually cropped and trained separately. Fine-tuning was performed using EfficientNets. The diagnostic ability of CNN was evaluated using 150 images with and without fractures from anteroposterior and lateral radiographs. The CNN model diagnosed DRF based on three views: frontal view, lateral view, and both frontal and lateral view. We determined the sensitivity, specificity, and accuracy of the CNN model, plotted a receiver operating characteristic (ROC) curve, and calculated the area under the ROC curve (AUC). We further compared performances between the CNN and three hand orthopedic surgeons. EfficientNet-B2 in the frontal view and EfficientNet-B4 in the lateral view showed highest accuracy on the validation dataset, and these models were used for combined views. The accuracy, sensitivity, and specificity of the CNN based on both anteroposterior and lateral radiographs were 99.3, 98.7, and 100, respectively. The accuracy of the CNN was equal to or better than that of three orthopedic surgeons. The AUC of the CNN on the combined views was 0.993. The CNN model exhibited high accuracy in the diagnosis of distal radius fracture with a plain radiograph. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|