Ghowsi Ali, Hatcher David, Suh Heeyeon, Wile David, Castro Wesley, Krueger Jan, Park Joorok, Oh Heesoo
Adjunct Assistant Professor, Department of Orthodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, Calif, USA.
Adjunct Associate Professor, Department of Orthodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, Calif, USA.
Angle Orthod. 2022 Sep 1;92(5):642-654. doi: 10.2319/122121-928.1. Epub 2022 Jun 2.
To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.
A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.
Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.
Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
评估一种全自动地标识别（ALI）系统作为自动地标定位工具相对于人工评判的准确性和可靠性。
共收集了100张锥束计算机断层扫描（CBCT）图像。在校准程序后，两名人工评判使用Checkpoint软件（Stratovan公司，加利福尼亚州戴维斯）在CBCT的x、y和z坐标平面上识别53个地标。通过对两名人工评判为每个地标识别的坐标进行平均来创建真实值。为评估ALI的准确性，确定了x、y和z坐标处的平均绝对误差（mm）以及人工地标识别与ALI之间的平均误差距离（mm），并计算了成功检测率。
总体而言，ALI系统在地标定位方面与人工评判一样成功。ALI所有坐标的平均绝对误差平均为1.57 mm。在所有三个坐标平面上，94%的地标平均绝对误差小于3 mm。所有53个地标平均误差距离为3.19±2.6 mm。当应用于100张CBCT上的53个地标时，ALI系统在4 mm误差距离范围内检测地标显示出75%的成功率。
总体而言，除了少数地标外，ALI显示出临床上可接受的平均误差距离。在不同时间对同一图像上的地标进行识别时，ALI比人工更精确。本研究证明了ALI在辅助正畸医生在CBCT上进行地标识别方面的前景。
