Department of Orthodontics, University Hospital of RWTH Aachen, Pauwelsstraße 30, D-52074, Aachen, Germany.
Department of Orthodontics, Witten/Herdecke University, Witten, Germany.
Clin Oral Investig. 2023 May;27(5):2255-2265. doi: 10.1007/s00784-023-04978-4. Epub 2023 Apr 4.
Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility.
A total of 931 CBCTs were used to train the algorithm. To test the algorithm, 35 landmarks were located manually by three experts and automatically by the algorithm in 114 CBCTs. The time and distance between the measured values and the ground truth previously determined by an orthodontist were analyzed. Intraindividual variations in manual localization of landmarks were determined using 50 CBCTs analyzed twice.
The results showed no statistically significant difference between the two measurement methods. Overall, with a mean error of 2.73 mm, the AI was 2.12% better and 95% faster than the experts. In the area of bilateral cranial structures, the AI was able to achieve better results than the experts on average.
The achieved accuracy of automatic landmark detection was in a clinically acceptable range, is comparable in precision to manual landmark determination, and requires less time.
Further enlargement of the database and continued development and optimization of the algorithm may lead to ubiquitous fully automated localization and analysis of CBCT datasets in future routine clinical practice.
由于数字化技术的不断发展，开发标准化且可重复的颅面结构全自动分析方法具有重要意义，这有助于减少诊断和治疗计划中的工作量，并生成客观数据。本研究旨在基于深度学习方法，开发一种全自动检测锥形束 CT（CBCT）中颅面标志的算法，并评估其在准确性、速度和可重复性方面的性能。
该算法共使用了 931 例 CBCT 进行训练。为了测试算法，由 3 名专家手动和算法自动在 114 例 CBCT 中定位了 35 个标志点。分析了测量值与之前由正畸医生确定的真实值之间的时间和距离。使用 50 例 CBCT 进行了两次分析，以确定手动定位标志点的个体内变异。
两种测量方法之间无统计学差异。总体而言，AI 的平均误差为 2.73mm，比专家的测量方法的平均误差低 2.12%，速度快 95%。在双侧颅面结构区域，AI 的平均结果优于专家。
自动标志点检测的准确性达到了临床可接受的范围，与手动标志点确定的精度相当，且所需时间更短。
进一步扩大数据库，并继续开发和优化算法，可能会导致在未来的常规临床实践中，实现对 CBCT 数据集的全自动定位和分析。
