Haptic dental simulators are gaining recognition for training dental students. However, there needs to be more evidence of their pedagogical effectiveness. The primary aims were to (1) identify the published studies related to the application of virtual reality (VR) and haptic technology in the restorative dentistry training of dental students, (2) recognize the outcome criteria used in the published studies, and (3) determine the subjective evaluation of VR and haptic technology in the restorative dentistry training by the students. A comprehensive literature search was conducted to find scholarly articles that assessed the utilization of VR and haptics in training students in restorative dentistry. The investigation was performed via seven online databases: Scopus, Web of Science Core Collection, PubMed, Science Direct Freedom Collection, Latin American & Caribbean Health Sciences Literature (LILACS), EMBASE, and MEDLINE. Of the 268 potential articles assessed, 22 met the inclusion criteria. Findings demonstrated feasibility and acceptability. Additionally, there was improved motor skill acquisition and retention and less time for dental restoration after haptic virtual reality training. With the rising evidence of efficacy and increased utilization of digital technologies, virtual reality, and haptics has a role in improving students’ education outcomes.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Conclusion Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Haptic dental simulators are gaining recognition for training dental students. However, there needs to be more evidence of their pedagogical effectiveness. The primary aims were to (1) identify the published studies related to the application of virtual reality (VR) and haptic technology in the restorative dentistry training of dental students, (2) recognize the outcome criteria used in the published studies, and (3) determine the subjective evaluation of VR and haptic technology in the restorative dentistry training by the students. A comprehensive literature search was conducted to find scholarly articles that assessed the utilization of VR and haptics in training students in restorative dentistry. The investigation was performed via seven online databases: Scopus, Web of Science Core Collection, PubMed, Science Direct Freedom Collection, Latin American & Caribbean Health Sciences Literature (LILACS), EMBASE, and MEDLINE. Of the 268 potential articles assessed, 22 met the inclusion criteria. Findings demonstrated feasibility and acceptability. Additionally, there was improved motor skill acquisition and retention and less time for dental restoration after haptic virtual reality training. With the rising evidence of efficacy and increased utilization of digital technologies, virtual reality, and haptics has a role in improving students’ education outcomes.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Conclusion Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Haptic dental simulators are gaining recognition for training dental students. However, there needs to be more evidence of their pedagogical effectiveness. The primary aims were to (1) identify the published studies related to the application of virtual reality (VR) and haptic technology in the restorative dentistry training of dental students, (2) recognize the outcome criteria used in the published studies, and (3) determine the subjective evaluation of VR and haptic technology in the restorative dentistry training by the students. A comprehensive literature search was conducted to find scholarly articles that assessed the utilization of VR and haptics in training students in restorative dentistry. The investigation was performed via seven online databases: Scopus, Web of Science Core Collection, PubMed, Science Direct Freedom Collection, Latin American & Caribbean Health Sciences Literature (LILACS), EMBASE, and MEDLINE. Of the 268 potential articles assessed, 22 met the inclusion criteria. Findings demonstrated feasibility and acceptability. Additionally, there was improved motor skill acquisition and retention and less time for dental restoration after haptic virtual reality training. With the rising evidence of efficacy and increased utilization of digital technologies, virtual reality, and haptics has a role in improving students’ education outcomes.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images. Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT. Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF. Conclusion Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Haptic dental simulators are gaining recognition for training dental students. However, there needs to be more evidence of their pedagogical effectiveness. The primary aims were to (1) identify the published studies related to the application of virtual reality (VR) and haptic technology in the restorative dentistry training of dental students, (2) recognize the outcome criteria used in the published studies, and (3) determine the subjective evaluation of VR and haptic technology in the restorative dentistry training by the students. A comprehensive literature search was conducted to find scholarly articles that assessed the utilization of VR and haptics in training students in restorative dentistry. The investigation was performed via seven online databases: Scopus, Web of Science Core Collection, PubMed, Science Direct Freedom Collection, Latin American & Caribbean Health Sciences Literature (LILACS), EMBASE, and MEDLINE. Of the 268 potential articles assessed, 22 met the inclusion criteria. Findings demonstrated feasibility and acceptability. Additionally, there was improved motor skill acquisition and retention and less time for dental restoration after haptic virtual reality training. With the rising evidence of efficacy and increased utilization of digital technologies, virtual reality, and haptics has a role in improving students’ education outcomes.

Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type ofAI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.

This systematic review evaluates current evidence regarding the feasibility of using needleless jet injection instead of a conventional local anesthetic needle. EBSCO, ProQuest, PubMed, and Scopus databases were used to identify relevant literature published in English from 2005 to 2020. Ten studies were selected. Five of them were randomized clinical trials, 3 case-control studies, and 2 equivalence trials. Using the Critical Appraisal Skills Program checklist, 6 studies scored between 67% and 100%, and 4 studies scored between 34% and 66%. According to Jadad’s scale, 2 studies were considered strong, and 8 studies were considered moderate in quality. The results of the 10 studies showed differences in patient preference for needleless jet injection. Needleless injection technique has been found to be particularly useful in uncooperative patients with anxiety and needle phobia.Needleless jet injection is not technique sensitive. However, with needleless jet anesthesia, most treatments require additional anesthesia. Conventional needle anesthesia is less costly, has a longer duration of action, and has better pain control during dental extraction. Needleless jet anesthesia has been shown to be moderately accepted by patients with a fear of needles, has a faster onset of action, and is an efficient alternative to conventional infiltration anesthesia technique.

Objectives: The purpose of the study was to assess the occurrence, location, and dimensions of the intraosseous vascular canal in the lateral wall of the maxillary antrum using cone-beam computed tomography (CBCT). Materials and Methods: In this retrospective study, we examined 400 CBCT scans from our archive of patients who had earlier reported to a dental teaching hospital in the United Arab Emirates. The prevalence, location, and dimensions of the lateral antral intraosseous canal (LAIC) in the maxillary antrum were evaluated by 2 examiners using standardised methods. A third examiner was consulted in cases of disagreement. Results: The prevalence of LAIC was 62.3% (249 maxillary antra) among the study population. The mean distance between the most inferior point of the alveolar bone and the inferior border of the LAIC in the posterior maxillary region was 19.83±3.12 mm. There was a significant difference (P=0.05) between the maxillary molar and premolar regions in mean distance from the most inferior point of the alveolar bone and the inferior border of the LAIC. There was no statistically significant difference in mean distance between the most inferior point of the alveolar bone and the inferior border of the LAIC between dentulous and edentulous areas (P=0.1). The G3-intrasinusal type canal less than 1mm in diameter was the most common type of LAIC. Conclusion This study established the approximate location of the LAIC in a United Arab Emirates cohort, which will assist the oral surgeon in selecting the appropriate site for sinus lift procedures with reduced risk of surgical hemorrhage.