Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs.

Journal: International journal of oral and maxillofacial surgery
PMID:

Abstract

The aim of this study was to develop automated models for the identification and detection of mandibular fractures in panoramic radiographs using convolutional neural network (CNN) algorithms. A total of 1710 panoramic radiograph images from the years 2016 to 2020, including 855 images containing mandibular fractures, were obtained retrospectively from the regional trauma centre. CNN-based classification models, DenseNet-169 and ResNet-50, were fabricated to identify fractures in the radiographic images. The CNN-based object detection models Faster R-CNN and YOLOv5 were trained to automate the placement of the bounding boxes to detect fractures in the radiographic images. The performance of the models was evaluated on a hold-out test set and also by comparison with residents in oral and maxillofacial surgery and oral and maxillofacial surgeons (experts) on a 100-image subset. The binary classification performance of the models achieved promising results with an area under the receiver operating characteristics curve (AUC), sensitivity, and specificity of 100%. The detection performance of the models achieved an AUC of approximately 90%. When compared with the accuracy of clinician observers, the identification performance of the models outperformed even an expert-level classification. In conclusion, CNN-based models identified mandibular fractures above expert-level performance. It is expected that these models will be used as an aid to improve clinician performance, with aided resident performance approximating that of expert level.

Authors

  • K Warin
    Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand. Electronic address: warin@tu.ac.th.
  • W Limprasert
    College of Interdisciplinary Studies, Thammasat University, Pathum Thani, Thailand.
  • S Suebnukarn
    Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand.
  • S Inglam
    Faculty of Dentistry, Thammasat University, Pathum Thani, Thailand. Electronic address: isamroeng@gmail.com.
  • P Jantana
    StoreMesh, Thailand Science Park, Pathum Thani, Thailand.
  • S Vicharueang
    StoreMesh, Thailand Science Park, Pathum Thani, Thailand. Electronic address: sothana.vcr@gmail.com.