Objectives: This study evaluated the feasibility of voice recognition-based electronic medical record (EMR) documentation for periodontal probing in dentistry, particularly emphasizing Korean-English bilingual speech patterns and real-world clinical conditions. Methods: Experiments were conducted in a dental chair setting during routine clinical hours. Environmental noise levels were measured, and two microphone types (stationary and pin-type) were evaluated. Periodontal probing phrases composed of three-digit numbers and positional terms were used for speech recognition. Consistent with common clinical practice in Korea, numerical values were spoken in Korean, whereas positional terms were spoken in English. Two speech-to-text application programming interfaces, Google Cloud Speech-to-Text and Naver Clova Speech Recognition, were assessed. Recognition accuracy was evaluated for both numerical components and complete bilingual phrases. Results: The mean environmental noise level was 60.65 dB and was minimally influenced by activity at adjacent dental chairs. The stationary microphone failed to capture speech effectively, whereas the pin-type microphone demonstrated stable recognition performance. For three-digit number recognition, accuracy was 88.3% with Google and 96.8% with Naver. For full-phrase recognition, complete matching was achieved in 36.7% of cases for Google and 52.5% for Naver. Partial recognition occurred more frequently for numerical components than for English positional terms. Conclusions Voice recognition-based EMR documentation for periodontal probing demonstrated preliminary feasibility in a dental clinical environment; however, performance was influenced by Korean-English bilingual speech patterns. These findings suggest that bilingual speech characteristics should be considered when implementing voice recognition systems in dental EMR workflows. Further optimization is required before routine clinical application.

This study aimed to assess the effectiveness of dental sound insulation in alleviating stress and fear during dental scaling in pediatric patients. It also examined the influence of a noise-canceling application on dentist-patient communication and convenience of dental procedures. This study included 60 children and adolescents aged 7 - 16 years between April 2022 and March 2023. All participants underwent dental plaque control using an ultrasonic scaler on the maxilla first, followed by plaque control on the mandible. Dental sound insulation with active noise canceling was randomly applied to either the maxilla or mandible. Findings revealed that the stress index was significantly reduced when the application was used, with a score of 5.85 compared to 8.43 without it (p < 0.0001). Similarly, the dental fear score was significantly reduced to 1.17 with the application, as opposed to 2.97 without it (p < 0.0001). The dental sound insulation did not affect the communication between dentists and patients or the convenience of treatment. This study demonstrated that active noise canceling during pediatric dental care significantly reduced stress and fear, suggesting that it could be a valuable behavior guidance tool, particularly for children who find dental visits challenging.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Purpose: This study aimed to develop an animal model suitable for coronectomy research. Materials and Methods: Eighteen Sprague-Dawley rats were divided into six groups: incisor control (InC), incisor flap (InF), incisor non-flap (InNF), molar control (MC), molar flap (MF), and molar non-flap (MNF). Coronectomy was not performed in the control groups (InC and MC). In the incisor (In) groups, coronectomy was performed on the mandibular incisors, with flap elevation in the InF group and without flap elevation in the InNF group. In the molar (M) groups, coronectomy was performed on the maxillary first molar, with flap elevation in the MF group and without flap elevation in the MNF group. The incisor groups were sacrificed on day 7, and the molar groups on days 7 and 14. Clinical healing, tooth movement, and histological and immunohistochemical analyses were performed. Results: InF and InNF groups showed tooth eruption similar to or the same as that before coronectomy, whereas the MF and MNF groups’ roots moved slowly. In InF and InNF groups, the pulp at the maturation zone was mineralized, but apical pulp vitality was maintained. MF and MNF groups showed bacterial infection and inflammation on day 7, with mineralization on day 14; however, apical pulp vitality was maintained. The MF group showed varied healing patterns, whereas the MNF group had consistent results across individuals. Conclusion Both incisors and molars are meaningful models for coronectomy. However, for consistent experimental results, coronectomy without flap elevation on the maxillary first molar is recommended.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Purpose: This study aimed to develop an animal model suitable for coronectomy research. Materials and Methods: Eighteen Sprague-Dawley rats were divided into six groups: incisor control (InC), incisor flap (InF), incisor non-flap (InNF), molar control (MC), molar flap (MF), and molar non-flap (MNF). Coronectomy was not performed in the control groups (InC and MC). In the incisor (In) groups, coronectomy was performed on the mandibular incisors, with flap elevation in the InF group and without flap elevation in the InNF group. In the molar (M) groups, coronectomy was performed on the maxillary first molar, with flap elevation in the MF group and without flap elevation in the MNF group. The incisor groups were sacrificed on day 7, and the molar groups on days 7 and 14. Clinical healing, tooth movement, and histological and immunohistochemical analyses were performed. Results: InF and InNF groups showed tooth eruption similar to or the same as that before coronectomy, whereas the MF and MNF groups’ roots moved slowly. In InF and InNF groups, the pulp at the maturation zone was mineralized, but apical pulp vitality was maintained. MF and MNF groups showed bacterial infection and inflammation on day 7, with mineralization on day 14; however, apical pulp vitality was maintained. The MF group showed varied healing patterns, whereas the MNF group had consistent results across individuals. Conclusion Both incisors and molars are meaningful models for coronectomy. However, for consistent experimental results, coronectomy without flap elevation on the maxillary first molar is recommended.

This study aimed to assess the effectiveness of dental sound insulation in alleviating stress and fear during dental scaling in pediatric patients. It also examined the influence of a noise-canceling application on dentist-patient communication and convenience of dental procedures. This study included 60 children and adolescents aged 7 - 16 years between April 2022 and March 2023. All participants underwent dental plaque control using an ultrasonic scaler on the maxilla first, followed by plaque control on the mandible. Dental sound insulation with active noise canceling was randomly applied to either the maxilla or mandible. Findings revealed that the stress index was significantly reduced when the application was used, with a score of 5.85 compared to 8.43 without it (p < 0.0001). Similarly, the dental fear score was significantly reduced to 1.17 with the application, as opposed to 2.97 without it (p < 0.0001). The dental sound insulation did not affect the communication between dentists and patients or the convenience of treatment. This study demonstrated that active noise canceling during pediatric dental care significantly reduced stress and fear, suggesting that it could be a valuable behavior guidance tool, particularly for children who find dental visits challenging.

Purpose: This study aimed to develop an animal model suitable for coronectomy research. Materials and Methods: Eighteen Sprague-Dawley rats were divided into six groups: incisor control (InC), incisor flap (InF), incisor non-flap (InNF), molar control (MC), molar flap (MF), and molar non-flap (MNF). Coronectomy was not performed in the control groups (InC and MC). In the incisor (In) groups, coronectomy was performed on the mandibular incisors, with flap elevation in the InF group and without flap elevation in the InNF group. In the molar (M) groups, coronectomy was performed on the maxillary first molar, with flap elevation in the MF group and without flap elevation in the MNF group. The incisor groups were sacrificed on day 7, and the molar groups on days 7 and 14. Clinical healing, tooth movement, and histological and immunohistochemical analyses were performed. Results: InF and InNF groups showed tooth eruption similar to or the same as that before coronectomy, whereas the MF and MNF groups’ roots moved slowly. In InF and InNF groups, the pulp at the maturation zone was mineralized, but apical pulp vitality was maintained. MF and MNF groups showed bacterial infection and inflammation on day 7, with mineralization on day 14; however, apical pulp vitality was maintained. The MF group showed varied healing patterns, whereas the MNF group had consistent results across individuals. Conclusion Both incisors and molars are meaningful models for coronectomy. However, for consistent experimental results, coronectomy without flap elevation on the maxillary first molar is recommended.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Purpose: This study aimed to develop an animal model suitable for coronectomy research. Materials and Methods: Eighteen Sprague-Dawley rats were divided into six groups: incisor control (InC), incisor flap (InF), incisor non-flap (InNF), molar control (MC), molar flap (MF), and molar non-flap (MNF). Coronectomy was not performed in the control groups (InC and MC). In the incisor (In) groups, coronectomy was performed on the mandibular incisors, with flap elevation in the InF group and without flap elevation in the InNF group. In the molar (M) groups, coronectomy was performed on the maxillary first molar, with flap elevation in the MF group and without flap elevation in the MNF group. The incisor groups were sacrificed on day 7, and the molar groups on days 7 and 14. Clinical healing, tooth movement, and histological and immunohistochemical analyses were performed. Results: InF and InNF groups showed tooth eruption similar to or the same as that before coronectomy, whereas the MF and MNF groups’ roots moved slowly. In InF and InNF groups, the pulp at the maturation zone was mineralized, but apical pulp vitality was maintained. MF and MNF groups showed bacterial infection and inflammation on day 7, with mineralization on day 14; however, apical pulp vitality was maintained. The MF group showed varied healing patterns, whereas the MNF group had consistent results across individuals. Conclusion Both incisors and molars are meaningful models for coronectomy. However, for consistent experimental results, coronectomy without flap elevation on the maxillary first molar is recommended.