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Background <p>A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APMP metrics and explore the feasibility of OP inclination in predicting changes in APMP.</p> Methods <p>Overall, 115 three-dimensional (3D) models were reconstructed using deep learning-based cone-beam computed tomography (CBCT) segmentation, and their accuracy in supporting cusps was compared with that of intraoral scanning models. The anatomical landmarks of seven OPs and three APMP metrics were identified, and their values were measured on the sagittal reference plane. The receiver operating characteristic curves of inclinations of seven OPs in distinguishing different anteroposterior skeletal patterns and correlations between inclinations of these OPs and APMP metrics were calculated and compared. For the OP inclination wit<i>h</i> the highest area under the curve (AUC) values and correlation coefficients, the regression models between this OP inclination and APMP metrics were further calculated.</p> Results <p>The deviations in supporting cusps between deep learning-based and intraoral scanning models were &lt; 0.300&#xa0;mm. The improved functional OP (IFOP) inclination could distinguish different skeletal classification determinations (AUC <sub>Class I <i>VS</i> Class II</sub> = 0.693, AUC <sub>Class I <i>VS</i> Class II<b>I</b></sub> = 0.763, AUC <sub>Class II <i>VS</i> Class II<b>I</b></sub> = 0.899, all <i>P</i> values &lt; 0.01) and the AUC value in skeletal Classes II and III determination was statistically higher than the inclinations of other OPs (all <i>P</i> values &lt; 0.01). Moreover, the IFOP inclination showed statistical correlations with APMP metrics (<i>r</i><sub>APDI</sub> = -0.557, <i>r</i><sub>ANB</sub> = 0.543, <i>r</i><sub>AF−BF</sub> = 0.731, all <i>P</i> values &lt; 0.001) and had the highest correlation coefficients among all OP inclinations (all <i>P</i> values &lt; 0.05). The regression analysis models of IFOP inclination and APMP metrics were y<sub>APDI</sub> = -0.917x + 91.144, y<sub>ANB</sub> = 0.395x + 0.292, and y<sub>AF−BF</sub> = 0.738x − 2.331.</p> Conclusions <p>Constructing the OP using deep learning-based 3D models from CBCT data is feasible. IFOP inclination could be used in predicting the APMP changes. A steeper IFOP inclination corresponded to a more retrognathic mandibular posture.</p>

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Feasibility of occlusal plane in predicting the changes in anteroposterior mandibular position: a comprehensive analysis using deep learning-based three-dimensional models

  • 期刊论文

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  • 发表日期：

    2025年1月8日

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  • Bingran Du,
  • Kaichen Li,
  • Zhiling Shen,
  • Yihang Cheng,
  • Jiayan Yu,
  • Yaopeng Pan,
  • Ziyan Huang,
  • Fei Hu,
  • Xiaohui Rausch-Fan,
  • Yuanpeng Zhu,
  • Xueyang Zhang

摘要

Background

A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APMP metrics and explore the feasibility of OP inclination in predicting changes in APMP.

Methods

Overall, 115 three-dimensional (3D) models were reconstructed using deep learning-based cone-beam computed tomography (CBCT) segmentation, and their accuracy in supporting cusps was compared with that of intraoral scanning models. The anatomical landmarks of seven OPs and three APMP metrics were identified, and their values were measured on the sagittal reference plane. The receiver operating characteristic curves of inclinations of seven OPs in distinguishing different anteroposterior skeletal patterns and correlations between inclinations of these OPs and APMP metrics were calculated and compared. For the OP inclination with the highest area under the curve (AUC) values and correlation coefficients, the regression models between this OP inclination and APMP metrics were further calculated.

Results

The deviations in supporting cusps between deep learning-based and intraoral scanning models were < 0.300 mm. The improved functional OP (IFOP) inclination could distinguish different skeletal classification determinations (AUC Class I VS Class II = 0.693, AUC Class I VS Class III = 0.763, AUC Class II VS Class III = 0.899, all P values < 0.01) and the AUC value in skeletal Classes II and III determination was statistically higher than the inclinations of other OPs (all P values < 0.01). Moreover, the IFOP inclination showed statistical correlations with APMP metrics (rAPDI = -0.557, rANB = 0.543, rAF−BF = 0.731, all P values < 0.001) and had the highest correlation coefficients among all OP inclinations (all P values < 0.05). The regression analysis models of IFOP inclination and APMP metrics were yAPDI = -0.917x + 91.144, yANB = 0.395x + 0.292, and yAF−BF = 0.738x − 2.331.

Conclusions

Constructing the OP using deep learning-based 3D models from CBCT data is feasible. IFOP inclination could be used in predicting the APMP changes. A steeper IFOP inclination corresponded to a more retrognathic mandibular posture.