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Purpose: The purpose of this study was to analyze the quantity and quality of the mandibular anterior alveolar bone in terms of alveolar width, density, and total alveolar height (TAH) based on dental status, gender, and age. Additionally, this study aimed to quantitatively evaluate the available alveolar height for graft harvesting (AHGH) and examine its variability based on the aforementioned factors. Materials and Methods: This retrospective cone-beam computed tomographic study included a total of 100 subjects. On the basis of gender, dental status, and age, the scans were divided into 3 primary groups and 8 subgroups. The mandibular alveolar width and density were measured 5 mm mesial to the mental foramen bilaterally and at the midline. The TAH was measured at the midline, and the AHGH was measured as the midline distance between 5 mm apical to the root of the canines and 5 mm superior to the lower border. Results: The mandibular alveolar width was statistically similar between dentulous and edentulous patients (P>0.05). A significantly greater density was observed at the midline in edentulous patients (P<0.05). The TAH was significantly greater in edentulous male patients than in edentulous female patients (P<0.05). Dentulous and male patients had significantly greater AHGH than edentulous and female patients, respectively (P<0.05). Conclusion: Based on the data evaluated in this study, it can be concluded that the mandibular symphyseal area has adequate bone quality and quantity for bone graft harvesting for dental implant therapy.

Objectives To evaluate the utility and efficiency of four voice-activated, artificial intelligence–based virtual assistants (Alexa, Google Assistant, Siri, and Cortana) in addressing commonly asked patient questions in orthodontic offices. Materials and Methods Two orthodontists, an orthodontic resident, an oral and maxillofacial radiologist, and a dental student used a standardized list of 12 questions to query and evaluate the four most common commercial virtual assistant devices. A modified Likert scale was used to evaluate their performance. Results Google Assistant had the lowest (best) mean score, followed by Siri, Alexa, and Cortana. The score of Google Assistant was significantly lower than Alexa and Cortana. There was significant variablity in virtual assistant response scores among the evaluators, with the exception of Amazon Alexa. Lower scores indicated superior efficiency and utility. Conclusions The common commercially available virtual assistants tested in this study showed significant differences in how they responded to users. There were also significant differences in their performance when responding to common orthodontic queries. An intelligent virtual assistant with evidence-based responses specifically curated for orthodontics may be a good solution to address this issue. The investigators in this study agreed that such a device would provide value to patients and clinicians.