Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Republic of Korea.
Private Clinic, Seongnam-si, Republic of Korea.
Sci Rep. 2023 Apr 11;13(1):5870. doi: 10.1038/s41598-023-33058-6.
The present study aimed to evaluate the performance of automated skeletal maturation assessment system for Fishman's skeletal maturity indicators (SMI) for the use in dental fields. Skeletal maturity is particularly important in orthodontics for the determination of treatment timing and method. SMI is widely used for this purpose, as it is less time-consuming and practical in clinical use compared to other methods. Thus, the existing automated skeletal age assessment system based on Greulich and Pyle and Tanner-Whitehouse3 methods was further developed to include SMI using artificial intelligence. This hybrid SMI-modified system consists of three major steps: (1) automated detection of region of interest; (2) automated evaluation of skeletal maturity of each region; and (3) SMI stage mapping. The primary validation was carried out using a dataset of 2593 hand-wrist radiographs, and the SMI mapping algorithm was adjusted accordingly. The performance of the final system was evaluated on a test dataset of 711 hand-wrist radiographs from a different institution. The system achieved a prediction accuracy of 0.772 and mean absolute error and root mean square error of 0.27 and 0.604, respectively, indicating a clinically reliable performance. Thus, it can be used to improve clinical efficiency and reproducibility of SMI prediction.
本研究旨在评估自动化骨骼成熟度评估系统在牙科领域中用于 Fishman 骨骼成熟度指标 (SMI) 的性能。骨骼成熟度在正畸学中对于确定治疗时机和方法非常重要。SMI 因其在临床应用中耗时更少且更实用而被广泛用于此目的。因此，现有的基于 Greulich 和 Pyle 以及 Tanner-Whitehouse3 方法的自动化骨骼年龄评估系统进一步开发为包括使用人工智能的 SMI。这种混合 SMI 修正系统由三个主要步骤组成：(1) 自动检测感兴趣区域；(2) 自动评估每个区域的骨骼成熟度；和 (3) SMI 阶段映射。主要验证是使用 2593 张手部 X 光片数据集进行的，并相应地调整了 SMI 映射算法。最终系统的性能在来自不同机构的 711 张手部 X 光片测试数据集上进行了评估。该系统的预测准确性为 0.772，平均绝对误差和均方根误差分别为 0.27 和 0.604，表明具有临床可靠的性能。因此，它可以用于提高 SMI 预测的临床效率和可重复性。
