Objectives
The aim of this study was to develop a computer vision algorithm based on artificial
intelligence, designed to automatically detect and classify various dental restorations
on panoramic radiographs.
Study Design
A total of 738 dental restorations in 83 anonymized panoramic images were analyzed.
Images were automatically cropped to obtain the region of interest containing maxillary
and mandibular alveolar ridges. Subsequently, the restorations were segmented by using
a local adaptive threshold. The segmented restorations were classified into 11 categories,
and the algorithm was trained to classify them. Numerical features based on the shape
and distribution of gray level values extracted by the algorithm were used for classifying
the restorations into different categories. Finally, a Cubic Support Vector Machine
algorithm with Error-Correcting Output Codes was used with a cross-validation approach
for the multiclass classification of the restorations according to these features.
Results
The algorithm detected 94.6% of the restorations. Classification eliminated all erroneous
marks, and ultimately, 90.5% of the restorations were marked on the image. The overall
accuracy of the classification stage in discriminating between the true restoration
categories was 93.6%.
Conclusions
This machine-learning algorithm demonstrated excellent performance in detecting and
classifying dental restorations on panoramic images.
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Article info
Publication history
Published online: July 07, 2020
Accepted:
May 22,
2020
Received in revised form:
April 28,
2020
Received:
January 8,
2020
Identification
Copyright
© 2020 Published by Elsevier Inc.
