Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.

OBJECTIVES: Machine learning (ML) algorithms are a portion of artificial intelligence that may be used to create more accurate algorithmic procedures for estimating an individual's dental age or defining an age classification. This study aims to use ML algorithms to evaluate the efficacy of pulp/tooth area ratio (PTR) in cone-beam CT (CBCT) images to predict dental age classification in adults. METHODS: CBCT images of 236 Turkish individuals (121 males and 115 females) from 18 to 70 years of age were included. PTRs were calculated for six teeth in each individual, and a total of 1416 PTRs encompassed the study dataset. Support vector machine, classification and regression tree, and random forest (RF) models for dental age classification were employed. The accuracy of these techniques was compared. To facilitate this evaluation process, the available data were partitioned into training and test datasets, maintaining a proportion of 70% for training and 30% for testing across the spectrum of ML algorithms employed. The correct classification performances of the trained models were evaluated. RESULTS: The models' performances were found to be low. The models' highest accuracy and confidence intervals were found to belong to the RF algorithm. CONCLUSIONS: According to our results, models were found to be low in performance but were considered as a different approach. We suggest examining the different parameters derived from different measuring techniques in the data obtained from CBCT images in order to develop ML algorithms for age classification in forensic situations.

Assessment of dental anxiety levels among dental emergency patients during the COVID­19 pandemic through the Modified Dental Anxiety Scale.

BACKGROUND: Coronavirus disease 2019 (COVID­19) continues to affect dental emergency services worldwide. Dental anxiety (DA) is described as a common and distressing problem in terms of oral health maintenance. OBJECTIVES: The present study aimed to evaluate DA levels as well as the COVID­19 fear and perception of control (COVID­19 FPC) in patients attending dental emergency clinics during the COVID­19 pandemic. MATERIAL AND METHODS: Sociodemographic, dental and medical data was obtained from the participants. A face-to-face questionnaire with questions referring to the reasons for the emergency dental visit, the visual pain scale, the Modified Dental Anxiety Scale (MDAS), and the COVID­19 Fear and Perception of Control Scale (COVID­19 FPCS) as well as additional questions concerning bruxism and a previous diagnosis of anxiety/panic attacks or depression was administered. The χ2 test was used to analyze the data. RESULTS: A total of 1,439 patients were included in the study. The most common reason for the dental visit was pain (47.5%). The prevalence of DA was 5.1% (74/1,439). A significant association was found between DA and gender (p = 0.020). The incidence of severe pain was higher in patients with DA than in those without DA (p = 0.002). No significant differences in the MDAS scores were found between patients with and without a chronic disease (p = 0.804), with regard to the educational status (p = 0.364), or between the age groups (p = 0.600). The prevalence of a 'strongly agree' response to all questions in COVID­19 FPCS was higher in patients with DA as compared to those without DA. CONCLUSIONS: Females and patients with severe pain were more likely to exhibit DA. In general, patients with DA strongly agreed with the statements of COVID­19 FPCS, which may indicate a correlation between the 2 scales.