Recently, machine learning methods have emerged to predict dental disease progression, often relying on costly annotated datasets and frequently exhibiting low generalization performance. This study evaluates the application of Siamese networks for detecting subtle changes in longitudinal dental x-rays and predicting the time span category between dental treatments using periapical radiographs and patient demographic data. We assume that the ability of these models to detect the time intervals between dental treatments would ensure their capability to identify more complex patterns related to disease progression. The baseline models based on CNNs and MLP achieved moderate performance, while the Siamese network models demonstrated significant improvements, with the highest-performing model achieving an accuracy of 86.32% ± 1.60%. Moreover, the introduction of demographic features such as age and gender into the model led to a significant reduction in performance variance. These results underscore the effectiveness of Siamese networks in capturing subtle temporal changes in dental radiographs in longitudinal settings, offering the potential to integrate these models into clinical workflows. Future research will explore self-supervised learning models for dental disease progression, especially in clinical settings with limited labeled data.