Thyroid

Significant Reduction in Manual Annotation Costs in Ultrasound Medical Image Database Construction Through Step by Step Artificial Intelligence Pre-annotation

This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model(AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used yolov8 as the A...

Enhancing Diagnostic Precision in Thyroid Nodule Classification: A Deep Learning Approach to Automated Ultrasound Image Analysis

Escalating thyroid nodule prevalence necessitates precise ultrasonographic diagnosis, which is constrained by operator-dependent variability. Convolutional neural network (CNN)-based artificial intelligence (AI)/machine learning (ML) frameworks can improve segmentation, malignancy prediction, and interobserver concordance, yet they often lack real-world clinical validation, interpretable architect...

Machine learning-Based Classification of Papillary Thyroid Carcinoma Versus Multinodular Goiter Using Preoperative Laboratory and Cytology Data

Thyroid nodules are frequently encountered in clinical practice, with their detection increasing due to advancements in imaging modalities. While most...

A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study.

PURPOSE: Thyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenomatoid nodules on ultrasound (TANU). Therefore, we a...

Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.

INTRODUCTION: Heart Failure (HF) complicated by thyroid dysfunction presents a complex clinical challenge, demanding more advanced risk stratification...

The value of a combined model based on ultra-radiomics and multi-modal ultrasound in the benign-malignant differentiation of C-TIRADS 4A thyroid nodules: a prospective multicenter study.

OBJECTIVE: To establish a combined model based on ultrasound radiomics combined with multimodal ultrasound and evaluate its value in diagnosing benign...

The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images.

OBJECTIVE: To extract and analyze the image features of two-dimensional ultrasound images and elastic images of four thyroid nodules by radiomics, and...

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM...

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

OBJECTIVE: Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspira...

CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images

Thyroid nodule segmentation in ultrasound images is crucial for accurate diagnosis and treatment planning. However, existing methods face challenges...

Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.

BACKGROUND: Hürthle cell (HCC) and columnar cell variants (CCV) are rare subtypes of thyroid cancer.

Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model

Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positi...

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

OBJECTIVE: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the rec...

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to for...

[Preoperative Evaluation of Cervical Lymph Node Metastasis in Patients With Hashimoto's Thyroiditis Combined With Thyroid Papillary Carcinoma Using Machine Learning and Radiomics-Based Features: A Preliminary Study].

OBJECTIVE: To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditi...

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

CONTEXT: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. ...

The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.

BACKGROUND: For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck diss...

Improved Diagnostic Accuracy of Thyroid Fine-Needle Aspiration Cytology with Artificial Intelligence Technology.

Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whol...

A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.

Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard a...

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This st...