Department of Artificial Intelligence and Software, Ewha Womans University, Seoul, South Korea.
Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University College of Dentistry, Seoul, Republic of Korea.
Sci Rep. 2024 Aug 16;14(1):18990. doi: 10.1038/s41598-024-69814-5.
Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies.
颞下颌关节紊乱是口腔颌面部不适的常见原因。诊断主要依赖于评估磁共振图像中颞下颌关节结构和位置。颞下颌关节的复杂解剖结构，加上磁共振图像质量的可变性，常常阻碍了准确的诊断。为了克服这一挑战，我们开发了专门用于自动分割颞下颌关节结构的深度学习模型，包括颞骨、关节盘和髁突。这些模型利用来自 542 名患者的 3693 张磁共振图像数据集进行了严格的训练和验证。在评估中，我们的集成模型（由五个单独的模型组合而成）在内部测试中对颞骨、关节盘、髁突和背景类别的平均 Dice 相似系数分别为 0.867、0.733、0.904 和 0.952。在外部验证中，颞骨、关节盘、髁突和背景的平均 Dice 相似系数值分别为 0.720、0.604、0.800 和 0.869。当应用于临床环境时，这些人工智能增强工具提高了医生的诊断准确性，尤其是在区分颞下颌关节前盘移位和骨关节炎时。总之，我们的深度学习方法实现的颞下颌关节自动分割是一种很有前途的方法，可以帮助完善颞下颌关节紊乱的诊断和治疗策略。
