Thyroid

FDE-net: Frequency-domain enhancement network using dynamic-scale dilated convolution for thyroid nodule segmentation.

Thyroid nodules, a common disease of endocrine system, have a probability of nearly 10% to turn into malignant nodules and thus pose a serious threat to health. Automatic segmentation of thyroid nodules is of great importance for clinicopathological diagnosis. This work proposes FDE-Net, a combined segmental frequency domain enhancement and dynamic scale cavity convolutional network for thyroid no...

Classification of neck tissues in OCT images by using convolutional neural network.

Identification and classification of surrounding neck tissues are very important in thyroid surgery. The advantages of optical coherence tomography (OCT), high resolution, non-invasion, and non-destruction make it have great potential in identifying different neck tissues during thyroidectomy. We studied the automatic classification for neck tissues in OCT images based on convolutional neural netw...

Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of rearrangement in papillary thyroid carcinoma.

PURPOSE: To create an ultrasound -based deep learning radiomics nomogram (DLRN) for preoperatively predicting the presence of rearrangement among pat...

The learning curve for single-port transaxillary robotic thyroidectomy (SP-TART): experience through initial 50 cases of lobectomy.

The new da Vinci® single-port (SP) robotic system, which utilizes a smaller incision and work space compared to the previous versions, is suitable for...

Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.

BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The ...

Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models.

OBJECTIVES: Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic...

Diseased thyroid tissue classification in OCT images using deep learning: Towards surgical decision support.

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue...

Detection of increased intracranial pressure in trans-oral robotic thyroidectomy using optic nerve sheath diameter measurement.

BACKGROUND: During transoral robot-assisted thyroidectomy, there is a risk of increasing intracranial pressure because the site of CO insufflation is ...

Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model.

BACKGROUND: Machine learning was a highly effective tool in model construction. We aim to establish a machine learning-based predictive model for pred...

Computational model of the full-length TSH receptor.

(GPCR)The receptor for TSH receptor (TSHR), a G protein coupled receptor (GPCR), is of particular interest as the primary antigen in autoimmune hypert...

An overview of deep learning applications in precocious puberty and thyroid dysfunction.

In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of ...

Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound ima...

A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model.

Core needle biopsy (CNB) is a method that can be used as an alternative to fine-needle aspiration for the sampling of thyroid nodules to provide a pre...

Objective assessment of segmentation models for thyroid ultrasound images.

Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation o...

FBCU-Net: A fine-grained context modeling network using boundary semantic features for medical image segmentation.

The performance of deep learning-based medical image segmentation methods largely depends on the segmentation accuracy of tissue boundaries. However, ...

Regulation of Quality of Life and Immune Function in Patients with Thyroid Cancer Treated by Deep Learning Technology.

BACKGROUND: In order to explore the regulation of quality of life and immune function in patients with thyroid cancer after radiotherapy, a method bas...

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms.

BACKGROUND: In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients underg...

A deep learning-based algorithm for tall cell detection in papillary thyroid carcinoma.

INTRODUCTION: According to the World Health Organization, the tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC) co...

Optimization assisted framework for thyroid detection and classification: A new ensemble technique.

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and de...

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasou...