AIMC Topic: Salivary Gland Neoplasms

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Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.

Scientific reports
This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scann...

Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.

BMC oral health
OBJECTIVES: Accurate preoperative discrimination of salivary gland pleomorphic adenoma (SPA) stromal subtypes is essential for therapeutic plannings. We aimed to establish and test machine learning (ML) models for classification of stromal subtypes i...

Nanophotonic sensors and AI for a new possible approach for accurate diagnosis of salivary glands tumors: a technical note.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Currently, diagnosing salivary gland tumors in their early stages presents significant challenges. This paper aims to outline a feasibility analysis of a novel approach utilizing advanced nanophotonic sensors and AI to address these diagnostic issues...

Ultrasound-based deep learning to differentiate salivary gland tumors.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: Accurate preoperative diagnosis is essential for selecting appropriate surgical interventions. This study aims to develop a deep learning model based on ultrasound (US) imaging to accurately differentiate between benign and malignant saliv...

A Recognition System for Diagnosing Salivary Gland Neoplasms Based on Vision Transformer.

The American journal of pathology
Salivary gland neoplasms (SGNs) represent a group of human neoplasms characterized by a remarkable cytomorphologic diversity, which frequently poses diagnostic challenges. Accurate histologic categorization of salivary gland tumors is crucial to make...

Prognostic prediction model for salivary gland carcinoma based on machine learning.

International journal of oral and maxillofacial surgery
Although rare overall, salivary gland carcinomas (SGCs) are among the most common oral and maxillofacial malignancies. The aim of this study was to develop a machine learning-based model to predict the survival of patients with SGC. Patients in whom ...

Development of machine learning models to predict lymph node metastases in major salivary gland cancers.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Indications for elective treatment of the neck in patients with major salivary gland cancers are still debated. Our purpose was to develop a machine learning (ML) model able to generate a predictive algorithm to identify lymph node meta...

Neural network combining with clinical ultrasonography: A new approach for classification of salivary gland tumors.

Head & neck
OBJECTIVE: Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the accuracy of the ultrasound-trained model to computed tomography or magnetic resonance imaging trained m...

Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning.

IEEE journal of biomedical and health informatics
Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predic...