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Neural Network Technologies for Age Estimation in Children from Orthopantomograms (a Pilot Study)
M P Poletaeva1, Yu V Vasilevsky2, D K Valetov3
1MD, PhD, Associate Professor, Department of Forensic Medicine, N.V. Sklifosovsky Institute of Clinical Medicine; I.M. Sechenov First Moscow State Medical University (Sechenov University), 8/2 Trubetskaya St., Moscow, 119991, Russia.
Abstract:
The aim of the study was to investigate the potential of using artificial intelligence technologies for age estimation in children from dental radiographs.
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 (R2), mean squared error (MSE), and mean absolute error (MAE).
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.
