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

TL;DR

This paper presents a Python-based software tool that automates Steiner's cephalometric analysis using machine learning to improve accuracy and efficiency in orthodontic diagnostics.

Contribution

The novel contribution is a fail-safe, retrainable machine learning-enhanced software for automated Steiner's analysis.

Findings

The software uses Tkinter, Pillow, and ML to automate landmark identification in cephalometric images.

It maintains clinical reliability while significantly reducing manual effort and variability.

The ML component allows the software to be corrected and retrained for improved performance.

Abstract

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.