The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.
Ali AltındağSerkan BahrilliÖzer Çelikİbrahim Şevki BayrakdarKaan OrhanPublished in: Diagnostics (Basel, Switzerland) (2024)
This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training ( n = 1929, 80% of the total), validation ( n = 240, 10% of the total), and test ( n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.
Keyphrases
- deep learning
- artificial intelligence
- convolutional neural network
- big data
- machine learning
- clinical decision support
- cone beam computed tomography
- loop mediated isothermal amplification
- randomized controlled trial
- real time pcr
- label free
- systematic review
- magnetic resonance imaging
- risk assessment
- computed tomography
- climate change
- high throughput
- single cell
- quantum dots
- positron emission tomography