Department of Orthodontics, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran.
Mianeh Technical and Engineering Faculty, University of Tabriz, Tabriz, 51666-14766, Iran.
Prog Orthod. 2024 Jun 24;25(1):28. doi: 10.1186/s40510-024-00527-1.
Determining the right time for orthodontic treatment is one of the most important factors affecting the treatment plan and its outcome. The aim of this study is to estimate the mandibular growth stage based on cervical vertebral maturation (CVM) in lateral cephalometric radiographs using artificial intelligence. Unlike previous studies, which use conventional CVM stage naming, our proposed method directly correlates cervical vertebrae with mandibular growth slope.
To conduct this study, first, information of people achieved in American Association of Orthodontics Foundation (AAOF) growth centers was assessed and after considering the entry and exit criteria, a total of 200 people, 108 women and 92 men, were included in the study. Then, the length of the mandible in the lateral cephalometric radiographs that were taken serially from the patients was calculated. The corresponding graphs were labeled based on the growth rate of the mandible in 3 stages; before the growth peak of puberty (pre-pubertal), during the growth peak of puberty (pubertal) and after the growth peak of puberty (post-pubertal). A total of 663 images were selected for evaluation using artificial intelligence. These images were evaluated with different deep learning-based artificial intelligence models considering the diagnostic measures of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). We also employed weighted kappa statistics.
In the diagnosis of pre-pubertal stage, the convolutional neural network (CNN) designed for this study has the higher sensitivity and NPV (0.84, 0.91 respectively) compared to ResNet-18 model. The ResNet-18 model had better performance in other diagnostic measures of the pre-pubertal stage and all measures in the pubertal and post-pubertal stages. The highest overall diagnostic accuracy was also obtained using ResNet-18 model with the amount of 87.5% compared to 81% in designed CNN.
The artificial intelligence model trained in this study can receive images of cervical vertebrae and predict mandibular growth status by classifying it into one of three groups; before the growth spurt (pre-pubertal), during the growth spurt (pubertal), and after the growth spurt (post-pubertal). The highest accuracy is in post-pubertal stage with the designed networks.
确定正畸治疗的最佳时机是影响治疗计划和结果的最重要因素之一。本研究旨在使用人工智能基于颈椎成熟度（CVM）来评估侧位头颅侧位片上的下颌生长阶段。与以往使用传统 CVM 阶段命名的研究不同，我们的方法直接将颈椎与下颌生长斜率相关联。
为了进行这项研究，首先评估了美国正畸协会基金会（AAOF）生长中心获得的信息，在考虑了进入和退出标准后，共有 200 人，108 名女性和 92 名男性被纳入研究。然后，计算了从患者连续拍摄的侧位头颅侧位片上的下颌长度。根据下颌在 3 个阶段的生长速度，将相应的图表标记为青春期前（pre-pubertal）、青春期高峰期（pubertal）和青春期后（post-pubertal）。共选择了 663 张图像用于人工智能评估。使用不同的基于深度学习的人工智能模型评估这些图像，考虑到诊断措施的敏感性、特异性、准确性、阳性预测值（PPV）和阴性预测值（NPV）。我们还采用了加权 kappa 统计。
在预青春期阶段的诊断中，与 ResNet-18 模型相比，为本研究设计的卷积神经网络（CNN）具有更高的敏感性和 NPV（分别为 0.84 和 0.91）。ResNet-18 模型在预青春期阶段的其他诊断措施以及青春期和青春期后阶段的所有措施中的表现都更好。使用 ResNet-18 模型获得的总体诊断准确性最高，为 87.5%，而设计的 CNN 为 81%。
本研究训练的人工智能模型可以接收颈椎图像，并通过将其分类为 3 组之一来预测下颌生长状态：生长突增前（pre-pubertal）、生长突增期间（pubertal）和生长突增后（post-pubertal）。使用设计的网络，最高的准确性是在青春期后阶段。
