ArticleJournal of pharmacy & bioallied sciences2025
Comparative Evaluation of Craniofacial Growth Patterns Using AI-Driven Longitudinal Cephalometric Superimpositions in Adolescents.
Article in Journal of pharmacy & bioallied sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cross-Domain Generalization of Deep Learning Architectures for Cephalometric Landmark Detection: A Dual-Dataset and Multi-Device Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- The Evolution and Impact of Artificial Intelligence in Orthognathic Surgery: A Bibliometric Analysis.Cureus · 2026Review
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Authors and funding
6 authors.
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Abstract
Background: Accurate analysis of craniofacial growth is essential for individualized orthodontic treatment planning. Traditional cephalometric methods often rely on manual tracing, which can be time-consuming and subject to interobserver variability. Recent advances in artificial intelligence (AI) offer promising tools for precise and reproducible assessment of growth changes. Methods: A longitudinal cohort of 90 adolescents (aged between 11 and 16 years) was analyzed, comprising 30 each from horizontal, vertical, and average growth pattern groups, determined by initial cephalometric assessment. AI algorithms were used to superimpose lateral cephalograms taken at baseline and after 18 months. Linear and angular measurements, including S-N, ANB, mandibular length (Co-Gn), and lower anterior facial height (ANS-Me), were computed. Statistical analysis involved ANOVA and Results: The AI-based analysis revealed significant differences in mandibular length increase among the groups (horizontal: 5.2 ± 1.3 mm, vertical: 3.1 ± 1.2 mm, average: 4.1 ± 1.1 mm; Conclusion: AI-driven cephalometric superimposition provides a reliable and efficient method for assessing craniofacial growth in adolescents. Vertical growers show greater vertical skeletal development, while horizontal growers exhibit more prominent mandibular lengthening. AI integration into routine cephalometric evaluation can significantly enhance diagnostic accuracy and growth prediction.
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