RESUMO
BACKGROUND AND OBJECTIVE: The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. MATERIALS AND METHODS: An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. RESULTS: Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. CONCLUSION: Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.
RESUMO
OBJECTIVES: The response of the host to plaque can be affected by systemic diseases like diabetes, hormonal changes, or immunological deficits, which can hasten the progression and severity of periodontitis. This study aimed to compare the activity of salivary matrix metalloproteinase-8 (MMP-8) in patients with moderate to severe generalized chronic generalized periodontitis between healthy individuals and those with type 2 diabetes who were referred to the Tabriz School of Dentistry. MATERIALS AND METHODS: For this cross-sectional study, 90 patients were randomly divided into three groups based on inclusion and exclusion criteria: patients with chronic generalized periodontitis with diabetes, patients with generalized chronic periodontal disease with normal blood glucose, and a control group of 30 healthy individuals. Participants were instructed not to brush their teeth for 12 h and not to eat or drink for 90 min before saliva sampling. Saliva samples were immediately stored at -80°C and analyzed using an ELISA test. RESULTS: The results showed that there was a significant difference in salivary MMP-8 levels among the three groups. Patients with periodontitis and diabetes had the highest levels of salivary MMP-8, while the control group had the lowest levels. This indicates that chronic generalized periodontitis is strongly associated with the activity level of salivary MMP-8, and elevated levels of MMP-8 in diabetic patients demonstrate the impact of diabetes on periodontal disease. CONCLUSION: This study highlights the importance of monitoring salivary MMP-8 levels in patients with periodontitis, especially those with diabetes. It also emphasizes the need for proper management of systemic diseases to prevent or slow down the progression of periodontal disease.
