Abstract：
Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90-94), 0.87 (95% CI, 0.84-89) and 0.78 (95% CI, 0.75-81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.
摘要：
背景：众所周知，口腔疾病如牙周（牙龈）疾病与各种全身性疾病和病症密切相关。深度学习的进步有可能为医疗保健做出重大贡献，特别是在依赖医学成像的领域。结合基于临床和实验室数据的非成像信息可以允许临床医生做出更全面和准确的决定。方法:这里,我们开发了一种多模式深度学习方法来预测口腔健康状况的系统性疾病和障碍。在第一阶段使用了双损失自动编码器，以从1188张全景X射线照片中提取与牙周病相关的特征。然后，在第二阶段,我们将图像特征与来自电子健康记录(EHR)的人口统计学数据和临床信息融合,以预测系统性疾病.我们使用接收器操作特性(ROC)和准确性来评估我们的模型。通过一个看不见的测试数据集进一步验证了该模型。调查结果：根据我们的调查结果，最准确预测的前三个章节，按顺序,是第三章，VI和IX。结果表明，该模型可以预测属于第三章的系统性疾病，VI和IX，AUC值为0.92（95%CI，0.90-94），0.87（95%CI，0.84-89）和0.78（95%CI，0.75-81），分别。为了评估模型的稳健性，我们对这些章节的未知测试数据集进行了评估，结果显示第三章的准确度为0.88、0.82和0.72，VI和IX，分别。解释：本研究表明，可以考虑将全景X射线照片和临床口腔特征相结合，以训练用于预测系统性疾病和障碍的融合深度学习模型。
