ArticleSovremennye tekhnologii v meditsine2026
Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study).
Article in Sovremennye tekhnologii v meditsine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Materials and Methods: A retrospective study was conducted, analyzing orthopantomograms of 322 children (173 female, 149 male) aged 4-16 years. Fourteen permanent mandibular teeth were annotated on each radiograph. Neural network training was performed by splitting the data into training and test sets at a ratio of 80:20; 5-fold cross-validation was used. Age estimation was approached as a regression task. The neural network training and validation were conducted in Python using the PyTorch library. The accuracy of age prediction was assessed using the coefficient of determination (R Results: The study showed that the developed machine learning model was highly accurate in age estimation in children. The mean absolute error across cross-validation was 0.92 years, which was significantly lower than the error associated with traditional manual methods.
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