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.

Background/Objective: Deep learning (DL) technology has shown potential in enhancing diagnostic accuracy in dentomaxillofacial radiology, particularly for detecting carious lesions, apical lesions, and periodontal bone loss. However, its effect on general dentists’ ability to detect radiographic bone loss (RBL) in clinical practice remains unclear. This study investigates the impact of the Denti.AI DL technology on general dentists’ ability to identify bone loss in intraoral radiographs, addressing this gap in the literature. Methods: Ten dentists from the university’s dental clinics independently assessed 26 intraoral radiographs (periapical and bitewing) for bone loss using a Likert scale probability index with and without DL assistance. The participants viewed images on identical monitors with controlled lighting. This study generated 3940 data points for analysis. The statistical analyses included receiver operating characteristic (ROC) curves, area under the curve (AUC), and ANOVA tests. Results: Most dentists showed minor improvement in detecting bone loss on periapical radiographs when using DL. For bitewing radiographs, only a few dentists showed minor improvement. Overall, the difference in diagnostic accuracy between evaluations with and without DL was minimal (0.008). The differences in AUC for periapical and bitewing radiographs were 0.031 and −0.009, respectively, and were not statistically significant. Conclusions: This study found no statistically significant improvement in experienced dentists’ diagnostic accuracy for detecting bone loss in intraoral radiographs when using Denti.AI deep learning technology.

Background: Permanently replacing missing teeth in the younger population is a clinical challenge. However, dental autotransplantation offers a viable treatment option in this demographic. To be performed predictably, it requires proper diagnoses, planning, and adherence to established guidelines in a multidisciplinary approach. Such guidelines should use evidence-based dentistry to anticipate and limit complications. The aim of this study is to evaluate the current literature on dental autotransplantation, discuss an evidence-based protocol, highlight steps for minimizing complications, and present a case report on two autotransplantations conducted at the Graduate Periodontology clinic at the University of North Carolina at Chapel Hill, Adams School of Dentistry, in the context of representative cases. Methods: A literature search using PubMed was conducted with the goal of constructing a comparative table on both survival and success rates of autotransplantation, providing a comparison of protocols and clinical studies and informing this narrative review. This review is illustrated by cases with the intention to highlight critical steps, limitations, and strengths of this mode of treatment. Results and Conclusions: Given the information presented, it is concluded that autotransplantation is a valuable treatment option provided established guidelines are followed. It is particularly useful for the younger population where implants or other options are not appropriate or feasible. This study also highlights some of the gaps in knowledge in autotransplantation which present opportunity for further studies to be developed.