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

Abstract

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.

Keywords: Artificial intelligence; Deep learning; Object detection; Panoramic radiography; Vertical root fracture.