(14) (PDF) Detection of the separated root canal instrument on panoramic radiograph
A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (LSTM) to detect the separated endodontic instruments on dental radiographs.
Panoramic radiographs from the hospital archive were retrospectively evaluated by two dentists. A total of 915 teeth, of which 417 are labeled as “separated instrument” and 498 are labeled as “healthy root canal treatment” were included. A total of six deep learning models, four of which are some varieties of CNN (Raw-CNN, Augmented-CNN, Gabor filtered-CNN, Gabor-filtered-augmented-CNN) and two of which are some varieties of LSTM model (Raw-LSTM, Augmented-LSTM) were trained based on several feature extraction methods with an applied or not applied an augmentation procedure. The diagnostic performances of the models were compared in terms of accuracy, sensitivity, specificity, positive and negative predictive value using ten-fold cross-validation. A McNemar’s tests was employed to figure out if there is a statistically significant difference between performances of the models. Receiver Operating Characteristic (ROC) curves were developed to assess the quality of the performance of the most promising model (Gabor filtered-CNN model) by exploring different cut-off levels in the last decision layer of the model.
The Gabor filtered-CNN model showed the highest accuracy (84.37 ± 2.79), sensitivity (81.26 ± 4.79), positive predictive value (84.16 ± 3.35) an
data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting
diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an
AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical
radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed
using the U-Net model implemented with the PyTorch library. The AI models based on deep learning
models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical
lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores
for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown
were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and
0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively;
sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively;
sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively.
The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for
their use in routine clinical processes as a clinical decision support system.
- January 2023
- Computational and Mathematical Methods in Medicine
This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry.
Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
The preliminary search yielded 2560 articles relevant enough to the paper’s purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.