Endocrinology

Thyroid

Latest AI and machine learning research in thyroid for healthcare professionals.

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Thyroid nodule classification in ultrasound imaging using deep transfer learning.

BACKGROUND: The accurate diagnosis of thyroid nodules represents a critical and frequently encounter...

Can Artificial Intelligence Software be Utilised for Thyroid Multi-Disciplinary Team Outcomes?

OBJECTIVES: ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ea...

Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.

PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laborato...

TEDML: a new machine learning (ML) approach for predicting thyroid eye disease and identifying key biomarkers.

Thyroid eye disease (TED) features immune infiltration and metabolic dysregulation. Understanding th...

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. L...

ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.

Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional dia...

Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration.

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major ...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for b...

Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RAD...

Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for e...

Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules.

OBJECTIVE: To assess the diagnostic performance of the GPT-4 model in comparison to resident physici...

Artificial Intelligence in CT for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer Patients: A Meta-analysis.

PURPOSE: This meta-analysis aims to evaluate the diagnostic performance of CT-based artificial intel...

Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training.

PURPOSE: This study explores a self-learning method as an auxiliary approach in residency training f...

Breaking barriers: noninvasive AI model for BRAF mutation identification.

OBJECTIVE: BRAF is the most common mutation found in thyroid cancer and is particularly associated w...

RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

OBJECTIVES: Radiology reports contain valuable information for research and audits, but relevant det...

A multicenter diagnostic study of thyroid nodule with Hashimoto's thyroiditis enabled by Hashimoto's thyroiditis nodule-artificial intelligence model.

OBJECTIVE: This study aimed to develop a Hashimoto's thyroiditis nodule-artificial intelligence (HTN...

Diagnosis and treatment of a rare bilateral primary thyroid cancer: a case report.

Preoperative ultrasound examination of thyroid nodules is the most economical and effective screenin...

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