AIMC Topic: Mouth Neoplasms
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CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning.
Scientific reports
Jul 2, 2025
Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accur...
Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.
Scientific reports
Jul 1, 2025
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui...
Impact of mHealth on Postoperative Quality of Life, Self-Management, and Dysfunction in Patients With Oral and Maxillofacial Tumors: Nonrandomized Controlled Trial.
JMIR mHealth and uHealth
Jun 25, 2025
BACKGROUND: With a focus on postoperative dysfunctions that may occur after maxillofacial tumor resection and the difficulties faced during home rehabilitation, we developed a mobile health app based on nurse-patient cooperation. The app extends reha...
Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection.
Scientific reports
Jun 3, 2025
Electrical impedance spectroscopy (EIS) is a powerful tool used to investigate the properties of materials and biological tissues. This study presents one of the first applications of EIS for the detection and classification of oral potentially malig...
Artificial intelligence based vision transformer application for grading histopathological images of oral epithelial dysplasia: a step towards AI-driven diagnosis.
BMC cancer
Apr 25, 2025
BACKGROUND: This study aimed to classify dysplastic and healthy oral epithelial histopathological images, according to WHO and binary grading systems, using the Vision Transformer (ViT) deep learning algorithm-a state-of-the-art Artificial Intelligen...
Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.
European journal of cancer (Oxford, England : 1990)
Apr 14, 2025
INTRODUCTION: Measuring the chromatin state of a tumor provides a powerful map of its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in chromatin accessibility to the patholo...
Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.
Lasers in surgery and medicine
Apr 8, 2025
OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Here, we prese...
Integration of the fluorescence based portable device with the AI tools for the real-time monitoring of oral mucosal lesions.
Scientific reports
Mar 25, 2025
There is a need for non-invasive, sensitive, real-time, and user-friendly optical devices integrated with artificial intelligence (AI) based tools for the detection of oral mucosal lesions at early stage. Research on the development of optical device...
Assessing the performance of an artificial intelligence based chatbot in the differential diagnosis of oral mucosal lesions: clinical validation study.
Clinical oral investigations
Mar 18, 2025
OBJECTIVES: Artificial intelligence (AI) is becoming more popular in medicine. The current study aims to investigate, primarily, if an AI-based chatbot, such as ChatGPT, could be a valid tool for assisting in establishing a differential diagnosis of ...
Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability.
Computers in biology and medicine
Mar 7, 2025
BACKGROUND AND OBJECTIVE: Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often produce inconsistent results. This study introduces the Oral Cancer Attention Network (OCANet), a U-Net...
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