Articular Eminence Mandibular Fossa and Artificial Intelligence: A Systematic Review

Abstract

Artificial intelligence has devastated almost every field, including health. Scientific publications concerning the medical field and AI have been increasing steadily in recent years, covering almost all medical specialties. This study aims to systematically synthesize current research and the influence of different artificial intelligence models on the exploration of the other side of the TMJ: the articular eminence-mandibular fossa complex, using medical imaging and/or other types of datasets. Several databases PubMed (PM), Web of Science (WS), Scopus (SC), Science Direct (SD), Springer (SR), Research Gates (RG), and Taylor and Francis on line (TF) were consulted for articles on the subject of « Articular Eminence-Mandibular Fossa and Artificial Intelligence », from 1945 to 2025. One hundred and thirty four (134) studies were identified, of which twenty one (21) were included, totaling more than 3574 patients between controls and patients with TMDs and more than 23000 different types of images. Papers retained used MRI, CBCT, or OPG imaging alone or in combination with other types of features (namely radiomics extracted from this area), without forgetting other categories of datasets in order to explore this part of TMJ. To achieve this goal, various artificial intelligence models were used, including Machine Learning models (Random forest, Decision Tree, XG Boost, KNN, SVM…), and Deep Learning algorithms (ANN, DenseNet-121, U-Net, Seg-Net, MobileNet V2, RestNet-101…). Artificial intelligence method can help in this sense by exploration of temporal cavity, segmentation of its parts, and detection of different pathologies that can affect the joint in general.