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Mouth Neoplasms

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Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

PloS one
Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and en...

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.

Scientific data
Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experi...

Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.

BMC oral health
BACKGROUND: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence.

Deep learning-enabled fluorescence imaging for surgical guidance: training for oral cancer depth quantification.

Journal of biomedical optics
SIGNIFICANCE: Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth p...

Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and e...

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.

Oral diseases
OBJECTIVE: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classif...

A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.

Journal of dental research
Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investig...

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Oral diseases
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.

The Future of Oral Oncology: How Artificial Intelligence is Redefining Surgical Procedures and Patient Management.

International dental journal
INTRODUCTION AND AIMS: The future of oral oncology is significantly influenced by the incorporation of artificial intelligence (AI) technologies, such as surgical robotics and early histopathological diagnosis and detection of diseases. This article ...

Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.

Medical & biological engineering & computing
This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanc...