Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study

Abstract

Objectives: Detection of vertical root fractures (VRFs) in their initial stages is a crucial issue, which prevents the propagation of injury to the adjacent supporting structures. Designing a suitable neural network-based model could be a useful method to diagnose the VRFs. The aim of this study was to design a probabilistic neural network (PNN) to diagnose the VRFs in intact and endodontically treated teeth of periapical and CBCT radiographs. Also, we have compared the efficacy of these two imaging techniques in the detection of VRFs. Methods: A total of 240 radiographs of teeth (120 radiographs of teeth with no VRFs and 120 teeth with vertical fractures, with half of the teeth in each category treated endodontically and the remaining half intact, i.e. not endodontically treated) were used in 3 groups for training and testing of the neural network as follows: Group 1, 180/60; Group 2, 120/120; and Group 3, 60/180. First, Daubechies 3 wavelet was applied to acquire the image analysis coefficients on two planes; then Gabor filters were used to extract the image characteristics, which were used to educate the PNN. The designed neural network was able to diagnose and classify teeth with and without VRFs. In addition, in order to determine the best training and test sets in the network, the variance of the function of network changes was manipulated at a range of 0-1 and the results were assessed in terms of the parameters evaluated, including sensitivity, specificity and accuracy. Results: In the periapical radiographs, the maximum accuracy, sensitivity and specificity values in the three groups were 70.00, 97.78 and 67.7%, respectively. These values in the CBCT images were 96.6, 93.3 and 100%, respectively, at the variance change range of 0.1-0.65. Conclusions: The designed neural network can be used as a proper model for the diagnosis of VRFs on CBCT images of endodontically treated and intact teeth; in this context, CBCT images are more effective than similar periapical radiographs. Limitations of this study are the use of sound one-rooted premolar teeth without carious lesions and dental fillings and not simulating the adjacent anatomic structures. Further in vitro work using a full-skull simulation for CBCT and skin/bone simulation is needed.

Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

“…A CNN trained on a limited amount of image segments showed discrimination ability similar to that of live dentists assessing periodontal bone loss with panoramic radiographs (Krois et al ). Johari et al () modelled a probabilistic neural network (PNN) to detect vertical root fractures in vital and endodontically treated teeth using periapical and CBCT radiographs. They confirmed that the neural network diagnosed fractures more effectively using CBCT images than periapical radiographs, suggesting that this model may benefit endodontic assessments (Johari et al ).…”

“…Johari et al () modelled a probabilistic neural network (PNN) to detect vertical root fractures in vital and endodontically treated teeth using periapical and CBCT radiographs. They confirmed that the neural network diagnosed fractures more effectively using CBCT images than periapical radiographs, suggesting that this model may benefit endodontic assessments (Johari et al ). Kositbowornchai et al () also created a neural network to diagnose vertical root fractures using intraoral digital radiographs and evaluated the diagnostic performance of their neural network.…”

Dental Images Recognition Technology and Applications: A Literature Review

“…The possible and future clinical applications of artificial intelligence and neural networks is the prediction of a phenomenon. Probabilistic neural networks can be used in dentistry to predict fractures, as Johari et al indicated, where a probabilistic neural network was designed to diagnose a fracture in endodontically treated teeth [48].…”

Radiomics and Machine Learning in Oral Healthcare

“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”