Choi Eunhye, Shin Seokwon, Lee Kijin, An Taejin, Lee Richard K, Kim Sunmin, Son Youngdoo, Kim Seong Teak
School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
Department of Industrial and Systems Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea.
Sci Rep. 2025 Jan 13;15(1):1823. doi: 10.1038/s41598-024-83750-4.
This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise. The model that integrated all joint noise data with imaging demonstrated the highest performance, achieving an F1-score of 0.72. Another model, which incorporated both imaging and crepitus, also achieved the same F1-score but had lower D recall (0.55 vs. 0.67) and N precision (0.71 vs. 0.74). The AI models outperformed orofacial pain specialists when provided with imaging alone or in combination with all joint noise data. These findings suggest that AI-enhanced DJD diagnosis using TMJ panoramic radiography and joint noise data offers a promising approach for early detection and improved patient care. The results underscore AI's capability to integrate diverse diagnostic factors, providing a comprehensive and accurate assessment that surpasses traditional methods.
本研究旨在开发一种人工智能（AI）模型，用于利用颞下颌关节（TMJ）全景X线摄影和关节杂音数据筛查退行性关节病（DJD）。共收集了2631张TMJ全景图像，在排除不确定病例和错误后，最终数据集为3908张图像（2127张正常（N）图像和1781张DJD（D）图像）。使用GoogleNet的AI模型通过图像数据、临床医生检测到的摩擦音和患者报告的关节杂音的六种不同组合进行评估。将所有关节杂音数据与成像相结合的模型表现最佳，F1分数达到0.72。另一个结合了成像和摩擦音的模型也达到了相同的F1分数，但D召回率较低（0.55对0.67），N精度较低（0.71对0.74）。当仅提供成像或与所有关节杂音数据结合时，AI模型的表现优于口腔颌面疼痛专家。这些发现表明，利用TMJ全景X线摄影和关节杂音数据进行AI增强的DJD诊断为早期检测和改善患者护理提供了一种有前景的方法。结果强调了AI整合多种诊断因素的能力，提供了超越传统方法的全面而准确的评估。
