Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis.

Objectives: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.

Methods: This retrospective cohort study included 180 TMJs from 90 patients with TMJ signs and symptoms. A radiomics platform was used to extract imaging features of disc displacements.

Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.

Radiomics is a quantitative tool for digital image analysis. This systematic review aims to investigate the scientific articles to evaluate the potential implications of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics applications in DMFR and human samples, in vivo study, a case reports/series if ≧5 samples were included, while case reports/series if < 5 samples, articles other than in English, abstracts without full text, and studies published before 2015 were excluded.

Regional variations in psoriatic arthritis: Insights from a nationwide multicenter analysis in Türkiye.

Objectives: The study aimed to investigate and compare clinical features, disease activity, and the overall disease burden among psoriatic arthritis (PsA) patients across seven distinct geographic regions in Türkiye.

Patients And Methods: A multicenter cross-sectional study involving 1,134 PsA patients from 25 referral centers across seven regions was conducted. Demographic and clinical characteristics, comorbidities, joint involvement, extra-articular manifestations, and disease activity measures were evaluated across regions.

Segmentation of periapical lesions with automatic deep learning on panoramic radiographs: an artificial intelligence study.

Periapical periodontitis may manifest as a radiographic lesion radiographically. Periapical lesions are amongst the most common dental pathologies that present as periapical radiolucencies on panoramic radiographs. The objective of this research is to assess the diagnostic accuracy of an artificial intelligence (AI) model based on U²-Net architecture in the detection of periapical lesions on dental panoramic radiographs and to determine whether they can be useful in aiding clinicians with diagnosis of periapical lesions and improving their clinical workflow.