Evaluation of Cone-Beam Computed Tomography Images with Artificial Intelligence

This study aims to evaluate the success of artificial intelligence models developed using convolutional neural network-based algorithms on CBCT images. Labeling was done by segmentation method for 15 different conditions including caries, restorative filling material, root-canal filling material, dental implant, implant supported crown, crown, pontic, impacted tooth, supernumerary tooth, residual root, osteosclerotic area, periapical lesion, radiolucent jaw lesion, radiopaque jaw lesion, and mixed appearing jaw lesion on the data set consisting of 300 CBCT images. In model development, the Mask R-CNN architecture and ResNet 101 model were used as a transfer learning method. The success metrics of the model were calculated with the confusion matrix method. When the F1 scores of the developed models were evaluated, the most successful dental implant was found to be 1, and the lowest F1 score was found to be a mixed appearing jaw lesion. F1 scores were respectively dental implant, root canal filling material, implant supported crown, restorative filling material, radiopaque jaw lesion, crown, pontic, impacted tooth, caries, residual tooth root, radiolucent jaw lesion, osteosclerotic area, periapical lesion, supernumerary tooth, for mixed appearing jaw lesion; 1 is 0.99, 0.98, 0.98, 0.97, 0.96, 0.96, 0.95, 0.94, 0.94, 0.94, 0.90, 0.90, 0.87, and 0.8. Interpreting CBCT images is a time-consuming process and requires expertise. In the era of digital transformation, artificial intelligence-based systems that can automatically evaluate images and convert them into report format as a decision support mechanism will contribute to reducing the workload of physicians, thus increasing the time allocated to the interpretation of pathologies.