Objectives: To determine the efficacy of a deep-learning (DL) tool in assisting dentists in detecting apical radiolucencies on periapical radiographs. Methods: Sixty-eight intraoral periapical radiographs with CBCT-proven presence or absence of apical radiolucencies were selected to serve as the testing subset. Eight readers examined the subset, denoted the positions of apical radiolucencies, and used a 5-point confidence scale to score each radiolucency. The same subset was assessed by readers under two conditions: with and without Denti.AI DL tool predictions. For the two sessions, the performance of the readers was compared. The comparison was performed with the alternate free response receiver operating characteristic (AFROC) methodology. Results: Localization of lesion accuracy (AFROC-AUC), specificity and sensitivity (by lesion) detection demonstrated improvements in the DL aided session in comparison with the unaided reading session. Subgroup performance analysis revealed an increase in sensitivity for small radiolucencies and in radiolucencies located apical to endodontically treated teeth.. Conclusion: The study revealed that the DL technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs. Advances in knowledge: DL tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.

Objectives:Proximal dental caries remains a prevalent disease with only modest detection rates by current diagnostic systems. Many new systems are available without controlled validation of diagnos...

The American Academy of Oral and Maxillofacial Radiology established an ad hoc committee to draft evidence-based recommendations and clinical guidance for the application of patient contact shielding during dentomaxillofacial imaging. The committee reviewed monographs and reports from radiation protection organizations and studies that reported radiation dose to gonads, breasts, and thyroid gland from dentomaxillofacial imaging. Considering the absence of radiation-induced heritable effects in humans and the negligible dose to the gonads and fetus from dentomaxillofacial imaging, the committee recommends discontinuing shielding of the gonads, pelvic structures, and fetuses during all dentomaxillofacial radiographic imaging procedures. On the basis of radiation doses from contemporaneous maxillofacial imaging, the committee considered that the risks from thyroid cancer are negligible and recommends that thyroid shielding not be used during intraoral, panoramic, cephalometric, and cone-beam computed tomographic imaging. This position statement informs and educates the reader on evolving radiation protection practices and provides simple, unequivocal guidance to dental personnel to implement these guidelines. State and local authorities should be contacted to update regulations to reflect these recommendations.

Objective: To investigate the differences in imaging characteristics and the diagnostic accuracy of 225 intraosseous jaw lesions on panoramic radiographs (PAN) versus cone beam computed tomography (CBCT). Methods: 225 sets of PAN and CBCT images with biopsy-proven histopathological diagnoses were retrospectively compared in terms of radiographic features and diagnostic accuracy. The imaging characteristics of PAN and CBCT were independently evaluated by two oral and maxillofacial radiologists who were required to answer 12 questions and provided up to three differential diagnoses with their confidence scores. Results: Odds ratios (ORs) were statistically significant for border cortication (OR = 1.521; p = .003) and border continuity (OR = 0.421; p = .001), involvement on neurovascular canals (OR = 2.424; p < .001), expansion (OR = 7.948; p < .001), cortical thinning (OR = 20.480; p < .001) as well as its destruction (OR = 25.022; p < .001) and root resorption (OR = 2.477; p < .001). Furthermore, imaging features in the posterior and mandibular regions showed better agreement than those in the anterior and maxillary regions, respectively. The diagnostic accuracy of the first differential diagnosis was higher on CBCT than on PAN (Observer 1:78.7 vs 64.4%; Observer 2: 78.7 vs 70.2% (p < .001)). The observers’ confidence scores were also higher at CBCT interpretation compared with PAN. Conclusions: CBCT demonstrated a greater number of imaging characteristics of intraosseous jaw lesions compared with PAN, especially in the anterior regions of both jaws and in the maxilla. Diagnostic accuracy is improved with CBCT compared to PAN, especially for lesions in the maxilla. Radiologists have greater confidence when using CBCT.

Background/Objective: Periodontal disease is a prevalent inflammatory condition affecting the supporting structures of teeth, with radiographic bone loss (RBL) being a critical diagnostic marker. The accurate and consistent evaluation of RBL is essential for the staging and grading of periodontitis, as outlined by the 2017 AAP/EFP Classification. Advanced tools such as deep learning (DL) technology, including Denti.AI, an FDA-cleared software utilizing convolutional neural networks (CNNs), offer the potential for enhancing diagnostic accuracy. This study evaluated the diagnostic accuracy of Denti.AI for detecting RBL in intraoral radiographs. Methods: A dataset of 39 intraoral radiographs (22 periapical and 17 bitewing), covering 316 tooth surfaces (123 periapical and 193 bitewing), was selected from a de-identified pool of 500 radiographs provided by Denti.AI. RBL was assessed using the 2017 AAP/EFP Classification. A consensus panel of three board-certified dental specialists served as the reference standard. Performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and mean absolute error (MAE), were calculated. Results: For periapical radiographs, Denti.AI achieved a sensitivity of 76%, specificity of 86%, PPV of 83%, NPV of 80%, and accuracy of 81%, with an MAE of 0.046%. For bitewing radiographs, sensitivity was 65%, specificity was 90%, PPV was 88%, NPV was 70%, and accuracy was 76%, with an MAE of 0.499 mm. Conclusions: Denti.AI demonstrated clinically acceptable performance in detecting RBL and shows potential as an adjunctive diagnostic tool, supporting clinical decision-making. While performance was robust for periapical radiographs, further optimization may enhance its accuracy for bitewing radiographs.