A comprehensive software solution for Steiner's cephalometric analysis: Integrating machine learning for enhanced accuracy

Cephalometric analysis is one of the most essential tools in orthodontic diagnosis and treatment planning, with Steiner's analysis being a gold standard for evaluating skeletal and dental relationships. However, manual landmark identification is time-consuming and prone to variability. This study introduces a Python-based software tool that automates Steiner's analysis using Tkinter for GUI, Pillow for image processing, and machine learning (ML) for landmark refinement. The software improves efficiency while maintaining clinical reliability, demonstrating potential for AI-assisted orthodontic diagnostics. Machine learning component makes it fail safe, software can be corrected with mistakes retrained.