Nik Ravesh Mahshid, Ameli Nazila, Lagravere Vich Manuel, Lai Hollis
School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Front Dent Med. 2025 Jun 26;6:1583455. doi: 10.3389/fdmed.2025.1583455. eCollection 2025.
Accurate assessment of midpalatal suture (MPS) maturation is critical in orthodontics, particularly for planning treatment strategies in patients with maxillary transverse deficiency (MTD). Although cone-beam computed tomography (CBCT) provides detailed imaging suitable for MPS classification, manual interpretation is often subjective and time-consuming.
This study aimed to develop and evaluate a lightweight two-dimensional convolutional neural network (2D CNN) for the automated classification of MPS maturation stages using axial CBCT slices. A retrospective dataset of CBCT images from 111 patients was annotated based on Angelieri's classification system and grouped into three clinically relevant categories: AB (Stages A and B), C, and DE (Stages D and E). A 9-layer CNN architecture was trained and evaluated using standard classification metrics and receiver operating characteristic (ROC) curve analysis.
The model achieved a test accuracy of 96.49%. Class-wise F1-scores were 0.95 for category AB, 1.00 for C, and 0.95 for DE. Area under the ROC curve (AUC) scores were 0.10 for AB, 0.62 for C, and 0.98 for DE. Lower AUC values in the early and transitional stages (AB and C) likely reflect known anatomical overlap and subjectivity in expert labeling.
These findings indicate that the proposed 2D CNN demonstrates high accuracy and robustness in classifying MPS maturation stages from CBCT images. Its compact architecture and strong performance suggest it is suitable for real-time clinical decision-making, particularly in identifying cases that may benefit from surgical intervention. Moreover, its lightweight design makes it adaptable for use in resource-limited settings. Future work will explore volumetric models to further enhance diagnostic reliability and confidence.
准确评估腭中缝（MPS）成熟度在正畸学中至关重要，特别是对于上颌横向发育不足（MTD）患者的治疗策略规划。尽管锥形束计算机断层扫描（CBCT）提供了适用于MPS分类的详细成像，但人工解读往往主观且耗时。
本研究旨在开发并评估一种轻量级二维卷积神经网络（2D CNN），用于使用轴向CBCT切片自动分类MPS成熟阶段。基于安杰利耶里分类系统对111例患者的CBCT图像回顾性数据集进行标注，并分为三个临床相关类别：AB（A和B阶段）、C以及DE（D和E阶段）。使用标准分类指标和受试者工作特征（ROC）曲线分析对一个9层CNN架构进行训练和评估。
该模型的测试准确率达到96.49%。AB类别的逐类F1分数为0.95，C类为1.00，DE类为0.95。ROC曲线下面积（AUC）分数，AB类为0.10，C类为0.62，DE类为0.98。早期和过渡阶段（AB和C）较低的AUC值可能反映了专家标注中已知的解剖重叠和主观性。
这些发现表明，所提出的2D CNN在从CBCT图像中分类MPS成熟阶段方面表现出高准确性和稳健性。其紧凑的架构和强大的性能表明它适用于实时临床决策，特别是在识别可能受益于手术干预的病例方面。此外，其轻量级设计使其适用于资源有限的环境。未来的工作将探索体积模型，以进一步提高诊断可靠性和可信度。
