Title

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

Authors

Vinay V Bedre*, Sushil Mahajan & Trilok Shrivastav

Affiliation

Department of Orthodontics, Peoples University Bhopal, Madhya Pradesh, India; *Corresponding author

Email

Vinay V Bedre - E-mail: vinay.bedre7022@gmail.com
Sushil Mahajan - E-mail: drsushilbmahajan@gmail.com

Trilok shrivastav - E-mail: drtrilokshrivastava@gmail.com

Article Type

Research Article

Date

Received October 1, 2025; Revised October 31, 2025; Accepted October 31, 2025, Published October 31, 2025

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.

Keywords

Cephalometrics, Steiner's analysis, machine learning, orthodontics, automated land marking

Citation

Bedre et al. Bioinformation 21(10): 3885-3888 (2025)

Edited by

Hiroj Bagde

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.