Thyroid

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 gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis region...

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 of Radiology Thyroid Imaging Reporting and Data S...

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 clinical application, many reasons, such as large out...

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 become one of the important diseases that endange...

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, whereas anaplastic thyroid carcinoma (ATC) is a leth...

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 to 55% showing incidental findings. Current workflo...

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a con...

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 development of alternative methods for improving the diagno...

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, including papillary thyroid carcinoma (PTC), tubercul...

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, provide detailed information concerning proper technique...

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 predict bone metastasis (BM) in patients with newly ...

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 acquiring data from the Memorial Sloan Kettering Cancer ...

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 of metastatic lymph nodes (LNs) and lymph node rat...

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 differentiate malignant and benign thyroid nodules on CT,...

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 accuracy, efficiency, and robustness in noncontrast-e...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it gr...

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma.

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel m...