AIMC Topic: Thyroid Nodule

Clear Filters Showing 1 to 10 of 161 articles

Automated thyroid nodule classification in ultrasound imaging using a hybrid vision transformer and Wasserstein GAN with gradient penalty.

Scientific reports
In this study, we present a novel hybrid model combining the Vision Transformer (ViT) and Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) for thyroid nodule detection in ultrasound images. While traditional methods, such a...

Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.

Scientific reports
The thyroid gland is prone to various diseases, including thyroid nodules. Ultrasound is the primary diagnostic tool, but classification accuracy is often limited by radiologist expertise. Integrating Artificial Intelligence, particularly Deep Learni...

A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Biomedical physics & engineering express
Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' ...

Optimizing Thyroid Nodule Management With Artificial Intelligence: Multicenter Retrospective Study on Reducing Unnecessary Fine Needle Aspirations.

JMIR medical informatics
BACKGROUND: Most artificial intelligence (AI) models for thyroid nodules are designed to screen for malignancy to guide further interventions; however, these models have not yet been fully implemented in clinical practice.

The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.

BMC cancer
BACKGROUND AND AIMS: More cases of thyroid micro-nodules have been diagnosed annually in recent years because of advancements in diagnostic technologies and increased public health awareness. To explore the application value of various machine learni...

Enhancing weakly supervised data augmentation networks for thyroid nodule assessment using traditional and doppler ultrasound images.

Computers in biology and medicine
Thyroid ultrasound (US) is an essential tool for detecting and characterizing thyroid nodules. In this study, we propose an innovative approach to enhance thyroid nodule assessment by integrating Doppler US images with grayscale US images through wea...

Preliminary analysis of AI-based thyroid nodule evaluation in a non-subspecialist endocrinology setting.

Endocrine
PURPOSE: Thyroid nodules are commonly evaluated using ultrasound-based risk stratification systems, which rely on subjective descriptors. Artificial intelligence (AI) may improve assessment, but its effectiveness in non-subspecialist settings is uncl...

A Deep Learning-Based Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: Pilot Results for Evaluating Thyroid Malignancy in Pediatric Cohorts.

Thyroid : official journal of the American Thyroid Association
Artificial intelligence (AI) models have shown promise in predicting malignant thyroid nodules in adults; however, research on deep learning (DL) for pediatric cases is limited. We evaluated the applicability of a DL-based model for assessing thyroi...

Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.

PloS one
Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there i...

Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.

BMJ open
OBJECTIVES: To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.