Deep Learning for the Radiographic Detection of Apical Lesions
Published online: May 31, 2019
Abstract
Introduction
We applied deep convolutional neural networks (CNNs) to detect apical lesions (ALs) on panoramic dental radiographs.
Methods
Based
on a synthesized data set of 2001 tooth segments from panoramic
radiographs, a custom-made 7-layer deep neural network, parameterized by
a total number of 4,299,651 weights, was trained and validated via 10
times repeated group shuffling. Hyperparameters were tuned using a grid
search. Our reference test was the majority vote of 6 independent
examiners who detected ALs on an ordinal scale (0, no AL; 1, widened
periodontal ligament, uncertain AL; 2, clearly detectable lesion,
certain AL). Metrics were the area under the receiver operating
characteristic curve (AUC), sensitivity, specificity, and
positive/negative predictive values. Subgroup analysis for tooth types
was performed, and different margins of agreement of the reference test
were applied (base case: 2; sensitivity analysis: 6).
Resu…
