Sex estimation from lateral cephalograms via a hybrid multimodel convolutional neural network

Key Points

• Sex estimation was achieved with high accuracy using machine learning techniques, enhancing diagnostic capabilities.
• The study reports 85% accuracy in sex classification from lateral cephalograms, based on deep learning methods.
• Observational analysis of cephalometric data utilized a hybrid convolutional neural network approach.
• These findings may enable better gender identification in clinical settings, aiding treatment planning.