用于测量基骨宽度的锥形束 CT 标志点检测：一项回顾性验证研究。

Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study.

机构信息

Department of Stomatology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, 518055, China.

Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China.

出版信息

BMC Oral Health. 2024 Sep 14;24(1):1091. doi: 10.1186/s12903-024-04798-2.

DOI:10.1186/s12903-024-04798-2

PMID:39277722

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11402203/

Abstract

BACKGROUND

Accurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements.

METHODS

Based on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model's prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC).

RESULTS

It took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94-0.97) and 0.98 (0.97-0.99), respectively.

CONCLUSION

The novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice.

摘要

背景

准确评估基骨宽度对于区分正常咬合个体和需要上颌扩弓的上颌横向发育不足患者至关重要。在此，我们评估了深度学习（DL）模型测量基骨宽度标志点的有效性，并评估了自动测量与手动测量的一致性。

方法

基于 U-Net 算法，开发了一个从粗到精的 DL 模型，并使用 80 个锥形束 CT（CBCT）图像进行训练。该模型在 10 个 CBCT 扫描上进行了验证，并在另外 34 个扫描上进行了测试。为了评估 DL 模型的性能，将其测量值与一位初级正畸医生的手动测量值进行比较，使用一致性相关系数（CCC）进行评估。

结果

该 DL 模型在仅 CBCT 图像上执行测量任务大约需要 1.5 秒。该框架的平均径向误差为 1.22±1.93mm，在 2.0、2.5、3.0 和 4.0mm 范围内的成功检测率分别为 71.34%、81.37%、86.77%和 91.18%。在 34 例中，DL 模型与手动测量的上颌基骨宽度和下颌基骨宽度距离的 CCC（95%置信区间）分别为 0.96（0.94-0.97）和 0.98（0.97-0.99）。