Deep Learning-Based Detection of Simulated Root Resorption in Scenarios Involving Image-Degrading Artifacts: An in Vitro Study.

BACKGROUND/AIM: This study developed a deep learning-based convolutional neural network (CNN) model for detecting external root resorption (ERR) in periapical radiographs and cone-beam computed tomography (CBCT) scans, particularly in the presence of image-degrading artifacts. MATERIAL AND METHODS: A total of 480 bovine incisors were allocated into four experimental conditions (n = 120) according to the presence and absence of root canal treatment and ERR. Resorption defects were made 5 mm from the root apex using a round diamond bur. All specimens were imaged using periapical radiography and CBCT under standardized acquisition protocols. CBCT images were processed using a post-processing CBCT software, with and without the Blooming Artifact Reduction (BAR 1) algorithm, and with a volumetric rendering reconstruction tool. A pre-trained AlexNet CNN was adapted using transfer learning for four-class image classification. The CNN performance was evaluated using standard classification metrics, including overall accuracy, precision, recall, and F1-score. RESULTS: CNN performance varied across imaging modalities. Periapical radiography yielded perfect classification (100% accuracy). High accuracy was also observed for CBCT without BAR 1 (95.83%) and CBCT with BAR 1 (97.92%). CBCT with three-dimensional reconstruction showed reduced performance (73.96%), particularly in endodontically treated teeth without root resorption. CONCLUSIONS: Deep learning-based CNN model demonstrated high diagnostic performance for detecting ERR, strongly influenced by image acquisition and processing protocols.