Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected fr...

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Bibliographic Details
Published in:Oral radiology Vol. 36; no. 4; pp. 337 - 343
Main Authors: Fukuda, Motoki, Inamoto, Kyoko, Shibata, Naoki, Ariji, Yoshiko, Yanashita, Yudai, Kutsuna, Shota, Nakata, Kazuhiko, Katsumata, Akitoshi, Fujita, Hiroshi, Ariji, Eiichiro
Format: Journal Article
Language:English
Published: Singapore Springer Singapore 01.10.2020
Springer Nature B.V
Subjects:
Artificial Intelligence
Cone-Beam Computed Tomography
Deep learning
Dentistry
Humans
Imaging
Medicine
Neural networks
Oral and Maxillofacial Surgery
Original Article
Radiography
Radiography, Panoramic
Radiology
Reproducibility of Results
Teeth
Tooth Fractures - diagnostic imaging
Tooth Root - diagnostic imaging
Deep learning
Vertical root fracture
Panoramic radiography
Artificial intelligence
Object detection
ISSN:0911-6028, 1613-9674, 1613-9674
Online Access:Get full text
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Summary:Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. Results Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. Conclusions The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
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ISSN:0911-6028
1613-9674
1613-9674
DOI:10.1007/s11282-019-00409-x