ArticleBioinformation2025
A comprehensive software solution for Steiner's cephalometric analysis: Integrating machine learning for enhanced accuracy.
Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
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.
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Registered trials
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