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Background Estimating age is essential in both the analysis of human skeletal remains and assessing live persons. The third molar develops over a longer period and is hence used in age estimation for subadults. Since dental age correlates with chronological age better than other growth markers, this study aims to assess the reliability of dental age assessed using the University of Texas (UT) age estimation method and modified Cameriere’s method. Methods It is a retrospective cross-sectional study in which the development and maturation of mandibular third molars were examined in 600 orthopantomograms (264 males and 336 females) of South Indian individuals (16–23 years). Dental age was estimated by using an Indian-specific formula based on maturity index value and the UT-age estimation  software program. The results were evaluated using the Student’s t-test for both methods and Pearson’s correlation test to compare chronological age with estimated dental age. Results Positive correlation was noted between chronological age and estimated dental age for males, females, and the total sample with highly statistically significant differences (p = 0.000). Modified Cameriere’s method underestimated dental age in samples ranging from less than 1 year to more than 2 years. UT-age estimation method underestimated age in samples above 20 years and overestimated age in samples below 20 years. The predictive classification of utilizing the maturity index was 79.17% accurate. Conclusions Dental age was negligibly over and underestimated in UT software method whereas it was overestimated in the modified Cameriere's method. To evaluate the reliability of these two methods, studies with larger sample sizes and population-specific data sets should be performed. Graphical Abstract

An adenomatoid odontogenic tumor (AOT) is an uncommon benign tumor of the oral cavity commonly found in the maxillary anterior region and is associated with impacted canines in young females. It rarely occurs in the mandibular region with no impacted or missing teeth. A 21-year-old female reported to the clinic with swelling on the right side of the face for the past six months with no history of pain. Radiographic features such as unilocular radiolucency with thinning of the cortical borders and considerable buccal cortical expansion, as well as some evidence of radiopaque specks were noted. Histopathological examination revealed cells with hyperchromatic nuclei, rosette-like structures with focal areas of calcified mass, and concentric rings resembling Liesegang rings, suggestive of AOT. The tumor was treated surgically by enucleation and cauterization. Although follicular type is a common variant, the tumor presented in this case was of extrafollicular type noted in the mandibular canine and premolar regions of a young female patient with no related impacted tooth.

Background and objective: There is a paradigm shift in the medical and dental fields due to the introduction of artificial intelligence (AI). Since AI has a potential impact on current and future practitioners, understanding the basic concept, working principle, and likely applications of AI as a diagnostic tool in Oral Medicine and Radiology is necessary for its widespread use. Therefore, this study aims to assess the knowledge, attitude, and perception of dental students and dentists regarding the possible applications of AI in the field of Oral Medicine and Radiology. Materials and methods: This was a cross-sectional questionnaire-based study comprising 15 questions circulated through Google Forms® to 460 dental students and professionals. The questionnaire collected demographic data of participants and assessed their knowledge, perception, and attitude about AI in Oral Medicine and Radiology answered using a 5-point Likert scale. Responses obtained were statistically analyzed using descriptive statistics and a chi-square test. Results: Out of 460 participants, majority had an idea about AI (94.13%) and its working principle (73.30%). Participants agreed that AI can be used in the diagnosis and formulating of treatment plans (88.47%), early detection of cancer (77.82%), forensic dentistry (74.13%), and as a prognostic (80.65%) and quality control tool (81.30%). A majority felt that AI should be incorporated into the dental curriculum (92.39%) and most of them were against suggesting AI in clinical incorporation (35.87%) with a fear that AI might replace the clinician in the future (76.52%). Conclusion: Based on the findings of the study, we strongly recommend that further research and insights into AI should be delivered through lectures, curricular courses, and scientific meetings to explore and increase awareness about this fascinating technology.

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