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PurposeThe aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT).MethodsCombining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python.ResultsThe periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars.ConclusionsWe demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Background : Enamel matrix derivative (EMD) has been considered to exert positive effects on wound healing, postoperative discomfort, and bone regeneration. This study investigated the efficacy of adjunctive EMD use in alveolar ridge preservation (ARP).Aim/Hypothesis : The aim of this randomized, controlled, parallel-arm study was to evaluate the (1) radiographic bone dimensional changes,(2) postoperative discomfort, and (3) early wound healing outcomes, following extraction of maxillary anterior teeth and treatment with and without the adjunctive use of EMD.Material and Methods : Thirty extraction sockets (with < 50% bone loss in the buccal bone plate) were randomly assigned to two groups-deproteinized bovine bone mineral with 10% collagen covered with two layers of a native bilayer collagen membrane with the adjunctive use of EMD (test group) and without EMD (control group). Bone dimensional changes were measured using cone beam computed tomography at 3 and 5 months after ARP. The severity and duration of pain and swelling were evaluated using self-reported questionnaires and soft tissue wound healing outcomes were assessed clinically. Chi-square tests, t -tests, and Mann-Whitney U tests were conducted to compare differences between the two groups.Results : Radiographic and clinical analyses showed no significant differences in horizontal and vertical bone dimensional changes and soft tissue wound healing outcomes (including spontaneous bleeding, persistent swelling, and ulceration) between the two groups. There were no significant differences in the severity of pain and swelling between the two groups, but the durations of pain (difference [Df] = 1.20, 95% CI = 0.33-2.06; P = 0.008) and swelling (Df = 1.06, 95% CI = 0.11-2.01; P = 0.029) were significantly reduced in the test group. Conclusion and Clinical Implications: ARP with the adjunctive use of EMD reduced the durations of postoperative pain and swelling following maxillary anterior teeth extraction.

Convolutional neural networks (CNNs), a particular type of deep learning architecture, are positioned to become one of the most transformative technologies for medical applications. The aim of the current study was to evaluate the efficacy of deep CNN algorithm for the identification and classification of dental implant systems. A total of 5390 panoramic and 5380 periapical radiographic images from 3 types of dental implant systems, with similar shape and internal conical connection, were randomly divided into training and validation dataset (80%) and a test dataset (20%). We performed image preprocessing and transfer learning techniques, based on fine-tuned and pre-trained deep CNN architecture (GoogLeNet Inception-v3). The test dataset was used to assess the accuracy, sensitivity, specificity, receiver operating characteristic curve, area under the receiver operating characteristic curve (AUC), and confusion matrix compared between deep CNN and periodontal specialist. We found that the deep CNN architecture (AUC = 0.971, 95% confidence interval 0.963–0.978) and board-certified periodontist (AUC = 0.925, 95% confidence interval 0.913–0.935) showed reliable classification accuracies. This study demonstrated that deep CNN architecture is useful for the identification and classification of dental implant systems using panoramic and periapical radiographic images.

ObjectivesThe aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)—odontogenic keratocysts, dentigerous cysts, and periapical cysts—using dental panoramic radiography and cone beam computed tomographic (CBCT) images based on a deep convolutional neural network (CNN).MethodsThe GoogLeNet Inception‐v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning. Diagnostic indices (area under the ROC curve [AUC], sensitivity, specificity, and confusion matrix with and without normalization) were calculated and compared between pretrained models using panoramic and CBCT images.ResultsThe pretrained model using CBCT images showed good diagnostic performance (AUC = 0.914, sensitivity = 96.1%, specificity = 77.1%), which was significantly greater than that achieved by other models using panoramic images (AUC = 0.847, sensitivity = 88.2%, specificity = 77.0%) (p = .014).ConclusionsThis study demonstrated that panoramic and CBCT image datasets, comprising three types of odontogenic OCLs, are effectively detected and diagnosed based on the deep CNN architecture. In particular, we found that the deep CNN architecture trained with CBCT images achieved higher diagnostic performance than that trained with panoramic images.

In this study, the efficacy of the automated deep convolutional neural network (DCNN) was evaluated for the classification of dental implant systems (DISs) and the accuracy of the performance was compared against that of dental professionals using dental radiographic images collected from three dental hospitals. A total of 11,980 panoramic and periapical radiographic images with six different types of DISs were divided into training (n = 9584) and testing (n = 2396) datasets. To compare the accuracy of the trained automated DCNN with dental professionals (including six board-certified periodontists, eight periodontology residents, and 11 residents not specialized in periodontology), 180 images were randomly selected from the test dataset. The accuracy of the automated DCNN based on the AUC, Youden index, sensitivity, and specificity, were 0.954, 0.808, 0.955, and 0.853, respectively. The automated DCNN outperformed most of the participating dental professionals, including board-certified periodontists, periodontal residents, and residents not specialized in periodontology. The automated DCNN was highly effective in classifying similar shapes of different types of DISs based on dental radiographic images. Further studies are necessary to determine the efficacy and feasibility of applying an automated DCNN in clinical practice.

Background: EMD has been considered to exert positive effects on wound healing, postoperative discomfort, and bone regeneration. Purpose: The aim of this randomized controlled clinical trial was to investigate and compare (a) horizontal and vertical bone dimensional changes, (b) early postoperative discomfort and soft tissue wound healing outcomes, and (c) treatment modalities for implant placement, following posterior maxillary alveolar ridge preservation (ARP) with and without adjunctive use of EMD. Methods: Twenty-eight participants were randomly assigned to three groups: extraction sockets filled with bovine bone mineral and membrane with EMD (test group 1, n = 10) and without EMD (test group 2, n = 10) and spontaneous healing (control group, n = 8). Alveolar bone dimensional changes were measured using cone-beam computed tomography 5 months after ARP, and postoperative pain and wound healing outcomes were also evaluated. Results: There were no significant differences in horizontal or vertical bone dimensional changes between test groups 1 (horizontal width changes at 1 mm apically below the alveolar ridge crest [HW]: −1.44 ± 0.54 mm) and 2 (HW: −1.42 ± 0.26 mm), but the changes at HW (−2.36 ± 1.03 mm) in the control group were significantly greater than those in test groups 1 and 2 (P < .05). Early postoperative discomfort and soft tissue wound healing outcomes were not significantly different between the two test groups. Furthermore, unlike the control group, both the test groups 1 and 2 were implanted without sinus floor elevation using the lateral approach. Conclusion: Within the limitations of this study, EMD failed to provide additional benefits in ARP in the posterior maxilla. K E Y W O R D S alveolar process, enamel matrix derivatives, randomized controlled trial, wound healing 1 | INTRODUCTION Alveolar bone resorption is mainly caused by both local inflammatory response after periodontal infection and the physiologic process of disuse atrophy. 1 In particular, the maxillary posterior region has relatively low bone density and is anatomically contiguous with the maxillary sinus; therefore, increased alveolar bone loss and pneumatization after tooth extraction are possible in this region. 1,2 Therefore, it is

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