AIMC Topic: Tongue

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AI-driven tongue image analysis for diagnosing and predicting coronary artery disease.

Scientific reports
Coronary artery disease is a leading cause of mortality worldwide, with current diagnostic methods presenting challenges of invasiveness and accuracy. This study investigates artificial intelligence-powered tongue image analysis as a novel, non-invas...

Detect pre-cancerous tongue lesions for early oral cancer diagnosis using deep learning algorithm.

Scientific reports
Precancerous tongue lesion is a prevalent, complex, and highly perilous kind of cancer. The tumour might be in the salivary glands, tonsils, neck, cheek, and mouth. Oral Cancer (OC) is commonly identified in advanced stages due to the limited accurac...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

Advanced deep feature engineering with crayfish optimization for diabetes detection using tongue images.

Scientific reports
Biomedical imaging has developed as a non-invasive and effective approach for early disease diagnosis and health monitoring. Diabetes mellitus (DM) is a severe metabolic disease with a high global incidence, characterized by the improper secretion of...

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

Scientific reports
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 (...

A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images.

Scientific reports
Early and accurate detection of oral cancer plays a pivotal role in improving patient outcomes. This research introduces a custom-designed, 19-layer convolutional neural network (CNN) for the automated diagnosis of oral cancer using clinical images o...

Segmentation of airways and soft tissues on panoramic radiographs using artificial intelligence technology.

BMC oral health
BACKGROUND: Segmentation of airways and soft tissues on panoramic radiographs is a challenging yet crucial task in dental diagnostics, as these regions can often be confused with fractures or other lesions due to superimposition. This study aimed to ...

Artificial intelligence in dysphagia assessment: evaluating lingual muscle composition in head and neck cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Oropharyngeal dysphagia is a common and debilitating condition in head and neck cancer (HNC) patients. This study aimed to evaluate the relationship between tongue muscle composition (quantity and quality) and the risk of dysphagia in non-su...

Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.

JMIR aging
BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with ...

Tongue shape classification based on IF-RCNet.

Scientific reports
The classification of tongue shapes is essential for objective tongue diagnoses. However, the accuracy of classification is influenced by numerous factors. First, considerable differences exist between individuals with the same tongue shape. Second, ...