The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography

Abstract

Background: Panoramic radiography is a standard diagnostic imaging method for dentists. However,
it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the
superimposed facial skeleton structures. The objective of this study was to develop a deep learning
algorithm that is capable of detecting mandibular fractures and trauma automatically and compare
its performance with general dentists.
Materials and Methods: This is a retrospective diagnostic test accuracy study. This study used
a two‑stage deep learning framework. To train the model, 190 panoramic images were collected
from four different sources. The mandible was first segmented using a U‑net model. Then, to detect
fractures, a model named Faster region‑based convolutional neural network was applied. In the end,
a comparison was made between the accuracy, specificity, and sensitivity of artificial intelligence
and general dentists in trauma diagnosis.
Results: The mAP50 and mAP75 for object detection were 98.66% and 57.90%, respectively. The
classification accuracy of the model was 91.67%. The sensitivity and specificity of the model were
100% and 83.33%, respectively. On the other hand, human‑level diagnostic accuracy, sensitivity, and
specificity were 87.22 ± 8.91, 82.22 ± 16.39, and 92.22 ± 6.33, respectively.
Conclusion: Our framework can provide a level of performance better than general dentists
when it comes to diagnosing trauma or fractures.
Key Words: Deep learning, dental radiography, mandibular fractures, panoramic radiography