Thyroid

A stacking ensemble deep learning approach to cancer type classification based on TCGA data.

Cancer tumor classification based on morphological characteristics alone has been shown to have seri...

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperth...

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers.

Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid...

A deep-learning model to assist thyroid nodule diagnosis and management.

A deep-learning model to assist thyroid nodule diagnosis and management - Authors' reply.

A deep-learning model to assist thyroid nodule diagnosis and management.

Comparison of British Thyroid Association, American College of Radiology TIRADS and Artificial Intelligence TIRADS with histological correlation: diagnostic performance for predicting thyroid malignancy and unnecessary fine needle aspiration rate.

OBJECTIVES: To compare diagnostic performance of British Thyroid Association (BTA), American College...

Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network.

Ultrasound is one of the critical methods for diagnosis and treatment in thyroid examination. In cli...

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer.

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has...

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, wh...

Automatic Detection of Thyroid and Adrenal Incidentals Using Radiology Reports and Deep Learning.

BACKGROUND: Computed tomography (CT) is commonly performed when evaluating trauma patients with up t...

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carc...

Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.

: At present, thyroid disorders have a great incidence in the worldwide population, so the developme...

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT.

OBJECTIVES: Cervical lymph nodes with internal cystic changes are seen with several pathologies, inc...

Prevention of non-recurrent laryngeal nerve injury in robotic thyroidectomy: imaging and technique.

INTRODUCTION: The aim of this report was to summarize observations, evaluate the feasibility, provid...

Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to pr...

Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma.

BACKGROUND: IBM's Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquir...

Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence.

This study analyzed the prognostic significance of clinico-pathologic factors, including the number ...

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differe...

Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks.

The purpose of this study is to develop a deep learning method for thyroid delineation with high acc...