Individualizing ANB angle based on Arab and German orthodontic cephalometric analysis: a cross-sectional multi-center...

One of the standard methods for classifying skeletal malocclusion patients is based on the individualized ANB (ANB_indv.), which was established in 1977 by Panagiotidis and Witt. However, this method was based on a limited sample size and on one ethnic group. Therefore, the primary objective of this study was to develop a new equation based on multiple ethnic groups and a larger sample size, which would enhance the metrics of the traditional equation by Panagiotidis and Witt (r = 0.80/ R² = 0.64). This study included 300 Arab patients and 623 German patients' lateral cephalogram images, with skeletal class I occlusion, and applied machine-learning regression models to predict the ANB_indv. In the analysis of each population separately, the results showed that using SNA and ML-NSL angles led to a lower correlation coefficient among the Arab patients' formula (R² = 0.59) and a higher correlation coefficient among the German patients' formula (R² = 0.73) compared to Panagiotidis and Witt's research (r = 0.80). Additionally, our results showed that the addition of the SN-Pg angle can significantly improve the r and adjusted R² (R² (Arab) = 0.78, R² (German) = 0.88). In the validation analysis, which included SNA, ML-NSL, and SN-Pg angle, the sensitivity of Calculated_ANB (ANB-ANB_indv.) was 78.4% for the Arab equation and 92.97% for the German equation. Our study demonstrates that the relatively large sample size included in this analysis enhanced the ability to predict and explain the ANB angle. Furthermore, this study established a new equation including the two ethnic groups, that improved the sensitivity of the Calculated_ANB by up to 89.85%.