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<p>In the field of orthodontics, the degree of fusion of the midpalatal suture (MPS) is a crucial factor in determining the most appropriate maxillary expansion technique. This study analyzed 600 cone-beam computed tomography (CBCT) scans from patients aged 4 to 25, covering all MPS maturity stages (35 in stage A, 113 in stage B, 186 in stage C, 163 in stage D, and 103 in stage E). A custom model integrated 3D convolutional neural networks (3D CNNs) for processing image data and fully connected networks for handling tabular data to classify MPS stages. Key factors including gender, age, cervical vertebra staging, dental age, palatal morphology, and MPS bone density ratio were evaluated for their correlation with MPS fusion. Ethical approval was secured, and images were quality-assessed by orthodontists and a maxillofacial imaging physician. The model shows great performance in the diagnosis with AUC over 0.95. The model aids in selecting appropriate expanders, favoring traditional rapid palatal expansion (RME) for unclosed MPS and bone-anchored RME or surgery for fused cases.</p>

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A dataset of midpalatal suture maturation stage in cone-beam computed tomography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月26日

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  • Zhigang Zuo,
  • Bosha Jia,
  • Yutong Xiao,
  • Yi Hua,
  • Yunlong Zhang,
  • Jianing Li,
  • Leyu Zhang,
  • Zhengjian Guo,
  • Shijie Zhang,
  • Yue Wang

摘要

In the field of orthodontics, the degree of fusion of the midpalatal suture (MPS) is a crucial factor in determining the most appropriate maxillary expansion technique. This study analyzed 600 cone-beam computed tomography (CBCT) scans from patients aged 4 to 25, covering all MPS maturity stages (35 in stage A, 113 in stage B, 186 in stage C, 163 in stage D, and 103 in stage E). A custom model integrated 3D convolutional neural networks (3D CNNs) for processing image data and fully connected networks for handling tabular data to classify MPS stages. Key factors including gender, age, cervical vertebra staging, dental age, palatal morphology, and MPS bone density ratio were evaluated for their correlation with MPS fusion. Ethical approval was secured, and images were quality-assessed by orthodontists and a maxillofacial imaging physician. The model shows great performance in the diagnosis with AUC over 0.95. The model aids in selecting appropriate expanders, favoring traditional rapid palatal expansion (RME) for unclosed MPS and bone-anchored RME or surgery for fused cases.