DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue.

This study aimed to develop and evaluate deep convolutional neural network (DCNN) models with Grad-CAM visualization for the automated classification with interpretability of tongue conditions-specifically glossitis and oral squamous cell carcinoma (OSCC)-using clinical tongue photographs, with a focus on their potential for early detection and telemedicine-based diagnostics. A total of 652 tongue images were categorized into normal control (n = 294), glossitis (n = 340), and OSCC (n = 17). Four pretrained DCNN architectures (VGG16, VGG19, ResNet50, ResNet152) were fine-tuned using transfer learning.

Clinical, psychological, and hematological factors predicting sleep bruxism in patients with temporomandibular disorders.

This cross-sectional observational study aimed to identify the predictors of sleep bruxism (SB) in patients with temporomandibular disorder (TMD) and to comprehensively investigate its association with clinical, sleep-related, psychological, and hematological factors. Seventy-nine patients with TMD (69 females and 10 males; mean age 45.46 ± 14.

Oral microbiome profiles of gingivitis and periodontitis by next-generation sequencing among a group of hospital patients in Korea: A cross-sectional study.

Objectives: The oral microbiome plays an important role in the development and progression of periodontal disease. The purpose of this study was to compare microbial profiles of oral cavities in good health, with gingivitis, and in a state of periodontitis, and to identify novel pathogens involved in periodontal diseases.

Methods: One hundred and two participants, including 33 healthy controls, 41 patients with gingivitis, and 28 patients with periodontitis, were included in this cross-sectional study.

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning.

Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to develop an automatic prediction model utilizing multimodal data to diagnose OSA among TMD patients.