Thyroid

From objective grouping to fuzzy reference intervals: A standardized machine learning approach for thyroid function tests.

BACKGROUND: Accurate interpretation of thyroid function tests (TFTs) requires reliable reference intervals (RIs). Indirect methods based on retrospective laboratory data are increasingly used, but current strategies face major limitations, including rigid age cut-offs, inconsistent partitioning, and lack of objective evaluation of subgrouping criteria. To address these challenges, we developed a n...

Detection of Lymph Node Metastasis in Thyroid Cancer Using Deep Learning and Second Harmonic Generation Imaging.

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, with a significant proportion of patients being susceptible to lymph node metastasis (LNM). The presence of LNM has been shown to accelerate tumor progression. Existing diagnostic approaches, such as ultrasonography and postoperative pathological analysis, exhibit limited sensitivity in detecting non-metastatic cases, ...

Roadmap for Representative Artificial Intelligence Models for Thyroid Cancer.

Artificial intelligence (AI) models in thyroid cancer are often not generalizable due to biased or non-representative training and validation datasets...

Enabling micro-assessments of skills in the simulated setting using temporal artificial intelligence-models.

BACKGROUND: Assessing skills in simulated settings is resource-intensive and lacks validated metrics. Advances in AI offer the potential for automated...

Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules.

PURPOSE: This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a pa...

A novel network for resolving subjective masking differences and accurate thyroid nodule diagnosis.

BACKGROUND: Over the past three decades, there has been a significant increase in the incidence of thyroid cancer. Ultrasound serves as a non-invasive...

Biomarkers associated with Hashimoto's thyroiditis induced by viral infection: An integrative bioinformatics and machine learning.

OBJECTIVE: Hashimoto's thyroiditis (HT) is a common disease characterized by autoimmune injury of the thyroid. Its pathogenesis entails complex intera...

Integrating Artificial Intelligence in Thyroid Nodule Management: Clinical Outcomes and Cost-Effectiveness Analysis.

OBJECTIVE: The increasing incidence of thyroid nodules (TN) raises concerns about overdiagnosis and overtreatment. This study evaluates the clinical a...

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.

BACKGROUND: Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumo...

Multiparametric ultrasound techniques are superior to AI-assisted ultrasound for assessment of solid thyroid nodules: a prospective study.

OBJECTIVES: To evaluate the diagnostic performance of multiparametric ultrasound (mpUS) and AI-assisted B-mode ultrasound (AI-US), and their potential...

Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis.

Lymph node micro-metastasis represents the initial stage of breast cancer spread or metastasis. However, the limited size of these hidden lesions rest...

Differentiated thyroid cancer and positron emission computed tomography: when, how and why?

INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering ...

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point ker...

The evolving therapeutic landscape of Graves' disease in adults: present and future.

The therapeutic landscape of Graves' hyperthyroidism has been rapidly evolving in the last few years. There has been a shift worldwide towards antithy...

Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fin...

Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.

Machine learning is now extensively implemented in medical imaging for preoperative risk stratification and post-therapeutic outcome assessment, enhan...

Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC) is the most common malignant tumor of the endocrine system. mutations occur in 40-60%, mutations in 10-15%, and d...

Differential Diagnosis of Papillary Thyroid Carcinoma and Nodular Goiter With Papillary Hyperplasia Using Hyperspectral Imaging Technology.

Papillary thyroid carcinoma (PTC) and nodular goiter with papillary hyperplasia (NGPH) share similar histological features, complicating both preopera...

Improved bio-inspired with machine learning computing approach for thyroid prediction.

Thyroid illness is widely recognised as a prevalent health condition that can result in a range of health disorders. Thyroid illnesses, namely hypothy...

Orbital CT deep learning models in thyroid eye disease rival medical specialists' performance in optic neuropathy prediction in a quaternary referral center and revealed impact of the bony walls.

PURPOSE: To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disea...