Temporomandibular disorders (TMD) are a group of orofacial pain conditions which are the most common non-dental pain complaint in the maxillofacial region. Due to the complexity of the etiology, the diagnosis and management of TMD remain a challenge where consensus is still lacking in many aspects. While clinical examination is considered the most important process in the diagnosis of TMD, imaging may serve as a valuable adjunct in selected cases. Depending on the type of TMD, many treatment modalities have been proposed, ranging from conservative options to open surgical procedures. In this review, the authors discuss the present thinking in the etiology and classification of TMD, followed by the diagnostic approach and the current trend and controversies in management.

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.

To run a systematic review and meta-analysis of randomized clinical trials aiming to answer the clinical question “which analgesic combination and dosage is potentially the most effective and safe for acute post-operative pain control after third molar surgery?”. A systematic search of computer databases and journals was performed. The search and the evaluations of articles were performed by 2 independent reviewers in 3 rounds. Randomized clinical trials related to analgesic combinations for acute post-operative pain control after lower third molar surgery that matched the selection criteria were evaluated to enter in the final review. Fourteen studies with 3521 subjects, with 10 groups (17 dosages) of analgesic combinations were included in the final review. The analgesic efficacy were presented by the objective pain measurements including sum of pain intensity at 6 hours (SPID6) and total pain relief at 6 hours (TOTPAR6). The SPID6 scores and TOTPAR6 scores of the reported analgesic combinations were ranged from 1.46 to 6.44 and 3.24 – 10.3, respectively. Ibuprofen 400mg with oxycodone HCL 5mg had superior efficacy (SPID6: 6.44, TOTPAR6: 9.31). Nausea was the most common adverse effect, with prevalence ranging from 0-55%. Ibuprofen 200mg with caffeine 100mg or 200mg had a reasonable analgesic effect with fewer side effects. This systematic review and meta-analysis may help clinicians in their choices of prescribing an analgesic combination for acute post-operative pain control after lower third molar surgery. It was found in this systematic review Ibuprofen 400mg combined with oxycodone HCL 5mg has superior analgesic efficacy when compared to the other analgesic combinations included in this study.