A dataset of midpalatal suture maturation stage in cone-beam computed tomography.
Authors
Affiliations (7)
Affiliations (7)
- Department of Orthodontics, Tianjin Medical University School and Hospital of Stomatological & Tianjin Key Laboratory of Oral Soft and Hard Tissues Restoration and Regeneration, No.12 Qixiangtai Road, Heping District, Tianjin, 300070, P. R. China.
- Tianjin Medical University Institute of Stomatology, No.12 Qixiangtai Road, Heping District, Tianjin, 300070, P. R. China.
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, P. R. China.
- Department of Oral and Maxillofacial Radiology, Tianjin Medical University School and Hospital of Stomatological & Tianjin Key Laboratory of Oral Soft and Hard Tissues Restoration and Regeneration, No.12 Qixiangtai Road, Heping District, Tianjin, 300070, P. R. China.
- Department of Pharmacology and Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, P. R. China. [email protected].
- Department of Orthodontics, Tianjin Medical University School and Hospital of Stomatological & Tianjin Key Laboratory of Oral Soft and Hard Tissues Restoration and Regeneration, No.12 Qixiangtai Road, Heping District, Tianjin, 300070, P. R. China. [email protected].
- Tianjin Medical University Institute of Stomatology, No.12 Qixiangtai Road, Heping District, Tianjin, 300070, P. R. China. [email protected].
Abstract
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.