Thyroid

AI-Assisted Sonography for the Diagnosis of Thyroid Nodules.

PURPOSE: To evaluate the diagnostic accuracy, clinical utility, and workflow integration of artificial intelligence (AI)-assisted sonography for the assessment of thyroid nodules and to assess the tool's potential for improving diagnostic consistency, reducing the number of unnecessary biopsies, and assisting with clinical decision-making. METHODS: Database searches were conducted to identify stud...

Mechanistic and Clinical Perspectives on Traditional Chinese Medicine in Thyroid Cancer Management.

Thyroid cancer is one of the most prevalent malignancies of the endocrine system, comprising various subtypes such as papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and the aggressive anaplastic thyroid carcinoma (ATC). Despite a generally favorable prognosis for PTC, recurrence, metastasis, and resistance to conventional therapies continu...

Identification of Epigenetic Regulator-Associated Genes in Keloid Disease Through Integrated Bulk and Single-Cell Transcriptomics With RT-qPCR Validation.

There exists a close correlation between epigenetic factors and the progression of keloid disease (KD). The research aims to identify the key genes li...

Multi-omics, machine learning, and molecular simulation identify CD44 as a candidate target in endocrine-disrupting chemical-associated thyroid cancer progression.

Endocrine-disrupting chemicals (EDCs) are widely present in the environment and consumer products and may disturb thyroid hormone homeostasis. However...

Application of Artificial Intelligence in Diagnosis and Management of Thyroid Disease.

Artificial intelligence (AI) is transforming thyroid care, with applications spanning ultrasound risk stratification, cytopathology, histopathology, r...

Assessing the performance of three large language models in thyroid cancer tumour board decision-making.

Large language models (LLMs) have potential to support clinical decision-making, but their role in thyroid cancer multidisciplinary team (MDT) meeting...

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroi...

Multimodal Ultrasound-Based Decision Support for Bethesda IV Thyroid Nodules: Integration of Clinical, Radiomics, Deep Learning, and Topological Features.

To develop and internally validate a multimodal ultrasound-based decision support framework for benign-malignant risk stratification of Bethesda IV th...

MSCT-Trans: A Multi-scale Convolutional Neural Network Token Transformer for Interpretable Ultrasound Image Classification.

Automated ultrasound image classification is increasingly important for clinical decision support in breast, thyroid and fetal screening. However, dep...

Comparative Performance of Multimodal Large Language Models in Grayscale Ultrasound-Based Classification of Thyroid Nodules.

BACKGROUND: Multimodal large language models (LLMs) are increasingly being explored for medical image analysis, but their relative performance in thyr...

Thyroid Year-in-Review 2025-2026: Precision Medicine and De-escalation in Contemporary Thyroidology.

The 'Thyroid: Year in Review', presented at the AACE 2026 annual meeting, synthesized practice-influencing peer-reviewed clinical research published b...

Clinical value of artificial intelligence ultrasound-assisted diagnostic system in the differential diagnosis of benign and malignant thyroid nodules: A retrospective case-control study.

Accurately distinguishing the benign thyroid nodules (BTNs) and malignant thyroid nodules (MTNs) is crucial for treatment planning and prognosis. This...

Artificial Intelligence in Thyroid Nodules and Cancer: Clinical Validation and Real-World Performance.

Thyroid nodules are detected in a large proportion of adults undergoing high-resolution ultrasonography, yet only a minority harbor clinically signifi...

Artificial intelligence-driven transformation in the management of atypia of undetermined significance (AUS) thyroid nodules.

Atypia of undetermined significance (AUS) thyroid nodules account for 10-30% of thyroid fine-needle aspiration (FNA) cytology results, with a malignan...

Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma Using a Deep Learning Radiomics Model Based on SAM3 Automatic Segmentation of Ultrasound Images: A Multicenter Cohort Study.

RATIONALE AND OBJECTIVES: We aimed to establish a Segment Anything Model 3 (SAM3) based on ultrasound images for automatic papillary thyroid microcarc...

Prediction of Incident Atrial Fibrillation and Association With Outcomes Using Routine Electronic Health Records in a Western Pacific Population.

BACKGROUND AND AIM: Atrial fibrillation (AF) affects over 37 million people internationally and confers increased risk of cardiovascular conditions. P...

A polyline searching-driven evolutionary AI for disease detection of medical imaging data.

Objective Accurate Ultrasound (US) prostate cancer (PCa) segmentation images hold significant value for organ interventional guidance and clinical...

Radiomics and machine learning analysis of ultrasound images in Bethesda IV thyroid nodules: a retrospective monocentric study.

Bethesda IV thyroid nodules remain a major diagnostic challenge because cytology cannot reliably distinguish benign from malignant follicular-patterne...

Automated segmentation of thyroid tissue and carotid artery in ultrasound videos using expert-in-the-loop deep learning: a foundational step toward AI-assisted thyroid diagnostics.

Ultrasound (US) is the primary imaging modality for thyroid evaluation, yet its diagnostic accuracy remains highly operator dependent. Automated ident...

Explainable AI-Assisted Multimodal Ultrasound Radiomics for Preoperative Risk Stratification of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma.

The objective was to develop and validate an explainable artificial intelligence (AI)-based multimodal approach for preoperative risk stratification o...