Kim Moon Jong, An Taegun, Cho Il-San, Joo Changhee, Park Ji Woon
Department of Oral Medicine, Gwanak Seoul National University Dental Hospital, Seoul, Republic of Korea.
Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.
J Oral Rehabil. 2025 Oct;52(10):1641-1650. doi: 10.1111/joor.14030. Epub 2025 May 14.
This study aimed to develop and evaluate an artificial intelligence (AI) model to predict long-term treatment outcomes in temporomandibular disorder (TMD) patients using clinical data and verify the value of adding haematologic data in enhancing predictive accuracy.
The medical records of 132 TMD patients who visited the clinic and underwent 6 months of non-invasive conservative treatment between 2013 and 2019 were included in this study. The clinical data and haematologic features were collected from medical records. A decision tree algorithm was employed for feature selection, followed by a deep neural network (DNN) to build the prediction model. The performance of the models based on the decision tree algorithm and DNN was evaluated.
The decision tree model achieved an accuracy of 90.6% and an F1-score of 0.800. The subjective pain-related features, along with haematologic markers associated with systemic inflammation, were proven to be important features in the decision tree model. The predictive performance of the DNN model improved as haematologic features were added, with the final model achieving an accuracy of 90.6% and an F1-score of 0.769.
This study showed the potential of machine learning models in predicting long-term TMD prognosis using clinical and haematological features. In addition, these findings highlight the importance of including both subjective pain assessments and systemic haematologic markers for the development of aetiology-based diagnostic systems for TMD to enhance clinical decision-making and prognosis prediction accuracy.
本研究旨在开发并评估一种人工智能（AI）模型，该模型利用临床数据预测颞下颌关节紊乱病（TMD）患者的长期治疗结果，并验证添加血液学数据对提高预测准确性的价值。
本研究纳入了2013年至2019年间到诊所就诊并接受6个月非侵入性保守治疗的132例TMD患者的病历。从病历中收集临床数据和血液学特征。采用决策树算法进行特征选择，随后使用深度神经网络（DNN）构建预测模型。评估基于决策树算法和DNN的模型性能。
决策树模型的准确率为90.6%，F1值为0.800。主观疼痛相关特征以及与全身炎症相关的血液学标志物被证明是决策树模型中的重要特征。随着血液学特征的加入，DNN模型的预测性能有所提高，最终模型的准确率为90.6%，F1值为0.769。
本研究显示了机器学习模型利用临床和血液学特征预测TMD长期预后的潜力。此外，这些发现突出了将主观疼痛评估和全身血液学标志物纳入基于病因的TMD诊断系统开发中的重要性，以提高临床决策和预后预测的准确性。
