# A study of correlations between cephalometric measurements in Koreans with normal occlusion by network analysis

## TL;DR

This study uses network analysis to explore correlations among cephalometric measurements in Koreans with normal occlusion, aiming to improve understanding of oral and maxillofacial anatomy.

## Contribution

The novel approach integrates nine cephalometric analyses and applies network analysis to reveal correlation structures in normal occlusion.

## Key findings

- Network analysis revealed clusters and inter/intra-correlation structures of 65 cephalometric variables.
- Weighted network and minimum spanning tree methods better revealed the correlation structure.
- Classical analyses focus on nine anatomical features, with some metrics grouped by geometry rather than clinical relevance.

## Abstract

Analyzing the correlation between cephalometric measurements is important for improving our understanding of the anatomy in the oral and maxillofacial region. To minimize bias resulting from the design of the input data and to establish a reference for malocclusion research, the aims of this study were to construct the input set by integrating nine cephalometric analyses and to study the correlation structure of cephalometric variables in Korean adults with normal occlusion. To analyze the complex correlation structure among 65 cephalometric variables, which were based on nine classical cephalometric analyses, network analysis was applied to data obtained from 735 adults (368 males, 367 females) aged 18–25 years with normal occlusion. The structure was better revealed through weighted network analysis and minimum spanning tree. Network analysis revealed cephalometric variable clusters and the inter- and intra-correlation structure. Some metrics were divided based on their geometric interpretation rather than their clinical significance. It was confirmed that various classical cephalometric analyses primarily focus on investigating nine anatomical features. Investigating the correlation between cephalometric variables through network analysis can significantly enhance our understanding of the anatomical characteristics in the oral and maxillofacial region, which is a crucial step in studying malocclusion using artificial intelligence.