AIMC Topic: Mouth Neoplasms
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Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.
Frontiers in immunology
Dec 3, 2024
INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived...
A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia.
British journal of cancer
Nov 30, 2024
BACKGROUND: Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its potential for malignant transformation and the lack of reliable prognostic markers. Current OED grading systems do not reliably predict transformation and s...
Deep learning for early diagnosis of oral cancer via smartphone and DSLR image analysis: a systematic review.
Expert review of medical devices
Nov 28, 2024
INTRODUCTION: Diagnosing oral cancer is crucial in healthcare, with technological advancements enhancing early detection and outcomes. This review examines the impact of handheld AI-based tools, focusing on Convolutional Neural Networks (CNNs) and th...
Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.
Scientific reports
Nov 19, 2024
Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their lives, there are several causes for poor and wrong diagnos...
Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.
Oral diseases
Nov 3, 2024
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...
Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.
PloS one
Nov 1, 2024
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...
Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.
Oral diseases
Oct 27, 2024
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.
Harnessing machine learning technique to authenticate differentially expressed genes in oral squamous cell carcinoma.
Oral surgery, oral medicine, oral pathology and oral radiology
Oct 17, 2024
OBJECTIVE: Advancements in early detection of the disease, prognosis and the development of therapeutic strategies necessitate tumor-specific biomarkers. Despite continuous efforts, no molecular marker has been proven to be an effective therapeutic t...
The Future of Oral Oncology: How Artificial Intelligence is Redefining Surgical Procedures and Patient Management.
International dental journal
Oct 12, 2024
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
Oct 10, 2024
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...
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