A Pilot Study on AI-Driven Age Estimation and Sex Determination in Greek Individuals

AI methods (machine learning and deep learning methods) presented promising results concerning the accuracy of dental age estimation and sex determination. Therefore, this pilot study aims to evaluate the efficacy of an artificial intelligence system to estimate age and determine sex in a Greek population sample. Panoramic radiographs from 110 adult subjects comprised this study's sample. Males and females were equally distributed (1/1) in the sample. The dental status of each patient was different. The sample's age ranged from 9 to 84 years of age, with a mean age of 48.87 years (±16.14 yrs). The methodology employed beta versions of convolutional neural networks (CNNs) developed by the University of Zagreb. Separate CNNs were trained on 4000 panoramic radiographs: one for sex estimation and another for age estimation. The AI program overestimated the subjects' age on average by 4.16 years. A statistically significant correlation was found between true and estimated sex (p-value < 0.001). In males, the rate of agreement was 56.36%, while for females it was 89.47% (z-test for two proportions; p-value < 0.001). For the overall sample, Kappa = 73.21%, indicating a very good agreement. The results concerning age estimation are not quite satisfactory and further research is needed.