Abdulqader Abbas Ahmed, Jiang Fulin, Almaqrami Bushra Sufyan, Cheng Fangyuan, Yu Jinghong, Qiu Yong, Li Juan
State Key Laboratory of Oral Diseases and National Center for Stomatology and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Chongqing University Three Gorges Hospital, Chongqing, China.
PLoS One. 2025 May 20;20(5):e0323776. doi: 10.1371/journal.pone.0323776. eCollection 2025.
To investigate the potential of artificial intelligence (AI) in Cervical Vertebral Maturation (CVM) staging, we developed and compared AI-based qualitative CVM and AI-based quantitative QCVM methods. A dataset of 3,600 lateral cephalometric images from 6 medical centers was divided into training, validation, and testing sets in an 8:1:1 ratio. The QCVM approach categorized images into six stages (QCVM I-IV) based on measurements from 13 cervical vertebral landmarks, while the qualitative method identified six stages (CS1-CS6) through morphological assessment of three cervical vertebrae. Statistical analyses evaluated the methods' performance, including the Pearson correlation coefficient, mean square error (MSE), success detection rate (SDR), precision-recall metrics, and the F1 score. For landmark prediction, our AI model demonstrated remarkable performance, achieving an SDR (error threshold of ≤ 1.0 mm) of 97.14% and with the mean prediction error across thirteen landmarks ranging narrowly from 0.17 to 0.55 mm. Based on the AI-predicted landmarks, the cervical vertebral measurements showed strong agreement with orthodontists, as indicated by a Pearson correlation coefficient of 0.98 and an MSE of 0.004. Besides, the CVM method attained an overall classification accuracy of 71.11%, while the QCVM method showed a higher accuracy of 78.33%. These findings suggest that the AI-based quantitative QCVM method offers superior performance, with higher agreement rates and classification accuracy compared to the AI-based qualitative CVM approach, indicating the fully automated QCVM model could give orthodontists a powerful tool to enhance cervical vertebral maturation staging.
为了研究人工智能（AI）在颈椎成熟度（CVM）分期中的潜力，我们开发并比较了基于AI的定性CVM方法和基于AI的定量QCVM方法。来自6个医疗中心的3600张头颅侧位片数据集按照8:1:1的比例分为训练集、验证集和测试集。QCVM方法根据13个颈椎标志点的测量结果将图像分为六个阶段（QCVM I-IV），而定性方法则通过对三个颈椎的形态学评估确定六个阶段（CS1-CS6）。统计分析评估了这些方法的性能，包括皮尔逊相关系数、均方误差（MSE）、成功检测率（SDR）、精确召回指标和F1分数。对于标志点预测，我们的AI模型表现出色，在误差阈值≤1.0毫米的情况下，成功检测率（SDR）达到97.14%，13个标志点的平均预测误差在0.17至0.55毫米之间。基于AI预测的标志点，颈椎测量结果与正畸医生的测量结果高度一致，皮尔逊相关系数为0.98，均方误差为0.004。此外，CVM方法的总体分类准确率为71.11%，而QCVM方法的准确率更高，为78.33%。这些发现表明，与基于AI的定性CVM方法相比，基于AI的定量QCVM方法具有更好的性能，一致性率和分类准确率更高，这表明全自动的QCVM模型可以为正畸医生提供一个强大的工具来加强颈椎成熟度分期。
