YOLOv5s_CBCT 模型在CBCT图像缺牙位置牙槽骨高度和宽度测量中的应用研究

doi:10.12337/zgkqzzxzz.2025.10.002

中国口腔种植学杂志 ›› 2025, Vol. 30 ›› Issue (5): 429-439.DOI: 10.12337/zgkqzzxzz.2025.10.002

YOLOv5s_CBCT 模型在CBCT图像缺牙位置牙槽骨高度和宽度测量中的应用研究

刘文琦¹, 魏文泉², 莫宏兵^1,3, 沈晓蓉⁴, 臧旖欣^1,3

¹佳木斯大学口腔医学院 154002;
²哈尔滨工程大学智能科学与工程学院 150001;
³佳木斯大学附属口腔医院 154002;
⁴北京航空航天大学自动化科学与电气工程学院 100191

收稿日期:2025-05-08 出版日期:2025-10-30 发布日期:2025-10-30
通讯作者: 莫宏兵,Email：mhbys@163.com,电话：0454-8625875
作者简介:刘文琦,硕士研究生在读,研究方向：口腔种植、口腔种植修复、口腔图像分析与处理;莫宏兵,副主任医师、硕士研究生导师,研究方向：口腔种植、口腔种植修复、口腔图像分析与处理
基金资助:
黑龙江省教育厅基本科研业务费人才培养（2023-KYYWF-0613）

Application research of YOLOv5s_CBCT model in measuring alveolar bone height and width in CBCT images of missing teeth

Liu Wenqi¹, Wei Wenquan², Mo Hongbing^1,3, Shen Xiaorong⁴, Zang Yixin^1,3

¹Stomatology College of Jiamusi University, Jiamusi 154002, Heilongjiang, China;
²College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China;
³Affiliated Stomatological Hospital of Jiamusi University, Jiamusi 154002, Heilongjiang, China;
⁴School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Received:2025-05-08 Online:2025-10-30 Published:2025-10-30
Contact: Mo Hongbing, Email: mhbys@163.com, Tel: 0086-454-8625875
Supported by:
Talent Cultivation of Basic Research Operating Expenses of Heilongjiang Provincial Department of Education (2023-KYYWF-0613)

摘要/Abstract

摘要： 目的探究基于YOLOv5s的人工智能模型（YOLOv5s_CBCT）对缺牙位置牙槽骨高度和宽度的测量价值,为口腔种植术前规划提供精准量化工具。方法基于跨阶段部分融合模块（C2f）和大选择性核模块（LSK）构建C2f_CBCT模块和LSK_CBCT模块,并使用空间到深度下采样模块（SPD）、幽灵卷积模块（GhostConv）、幽灵C3模块（C3Ghost）和双向特征金字塔网络（BiFPN）等模块和结构,提出YOLOv5s_CBCT模型,将测试图像输入到训练完成后的YOLOv5s_CBCT模型中,分别得出模型对4个种植区域牙槽骨高度和宽度的预测平均值并进行误差分析;使用双向方差分析探究影响测量结果的显著因素,并进一步采用Bland-Altman一致性分析和Tukey检验来加强和完善双向方差分析的结论。结果（1）模型对比实验表明,YOLOv5s_CBCT模型的检测精度更高,且参数量和计算量更低。（2）模型对4个种植区域中牙槽骨高度检测的平均值误差最大为0.15 mm,最小为0.03 mm,对宽度检测的平均值误差最大为0.26 mm,最小为0.10 mm。（3）双向方差分析结果表明,牙槽骨高度和宽度的检测结果受到种植区域的显著影响,而不受测量方式的影响。（4）Bland-Altman一致性分析进一步证明了人工测量和模型预测具有较好的一致性。（5）Tukey检验确定了对测量结果产生显著影响的种植区域的分组情况。结论 YOLOv5s_CBCT模型的测量值误差均在可接受范围内,测量结果具有较高的临床参考价值,且YOLOv5s_CBCT模型便于在医学仪器上部署。

关键词: 口腔种植, 牙槽骨, 锥形束计算机体层成像, 人工智能, YOLO第五代小型版模型

Abstract: Objective To explore the measurement value of the artificial intelligence model based on YOLOv5s (YOLOv5s_CBCT) for the height and width of alveolar bone at the location of missing teeth, and to provide a precise quantitative tool for preoperative planning of oral implants. Methods Based on the faster implementation of CSP bottleneck with 2 convolutions (C2f), and large selective kernel (LSK) modules, the C2f_CBCT and LSK_CBCT modules were constructed. Using modules and structures such as space-to-depth (SPD), ghost convolution (GhostConv), C3 with ghost convolution (C3Ghost) and bidirectional feature pyramid network (BiFPN), the YOLOv5s_CBCT model was proposed, and the test images were input into the YOLOv5s_CBCT model after training. The predicted average values of alveolar bone height and width for the four implant areas by the model were respectively obtained and error analysis was conducted. Bidirectional analysis of variance was used to explore the significant factors influencing the measurement results, and Bland-Altman consistency analysis and Tukey test were further adopted to strengthen and improve the conclusions of bidirectional analysis of variance. Results (1) Model comparison experiments showed that the proposed model achieved higher detection accuracy with fewer parameters and lower computational complexity. (2) The maximum average error of alveolar bone height detection in the four implant regions was 0.15 mm, and the minimum was 0.03 mm. The maximum average error of width detection was 0.26 mm, and the minimum was 0.10 mm. (3) The results of the two-way ANOVA indicated that the detection results of alveolar bone height and width were significantly affected by the implant region, but not by the measurement method. (4) Bland-Altman consistency analysis further demonstrated good agreement between manual measurements and model prediction. (5) The Tukey test identified the grouping of implant regions that had a significant impact on the measurement results. Conclusion The overall experimental results demonstrated that the measurement errors of the model were all within an acceptable range. The measurement results have high clinical reference value, and the proposed YOLOv5s_CBCT model can be conveniently deployed on medical instruments.

Key words: Dental implants, Alveolar bone, Cone beam computed tomography, Artificial intelligence, YOLOv5s

刘文琦,魏文泉,莫宏兵,等. YOLOv5s_CBCT 模型在CBCT图像缺牙位置牙槽骨高度和宽度测量中的应用研究[J]. 中国口腔种植学杂志, 2025, 30(5): 429-439. DOI: 10.12337/zgkqzzxzz.2025.10.002

Liu Wenqi, Wei Wenquan, Mo Hongbing, Shen Xiaorong, Zang Yixin. Application research of YOLOv5s_CBCT model in measuring alveolar bone height and width in CBCT images of missing teeth[J].Chinese Journal of Oral Implantology, 2025, 30(5): 429-439.DOI: 10.12337/zgkqzzxzz.2025.10.002.

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YOLOv5s_CBCT 模型在CBCT图像缺牙位置牙槽骨高度和宽度测量中的应用研究

Application research of YOLOv5s_CBCT model in measuring alveolar bone height and width in CBCT images of missing teeth

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