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Related Concept Videos

The Thyroid Gland01:23

The Thyroid Gland

4.2K
The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Synthesis and Regulation of Thyroid Hormones01:20

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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

Salvatore Sorrenti1, Vincenzo Dolcetti2, Maija Radzina3,4

  • 1Department of Surgical Sciences, "Sapienza" University of Rome, 00161 Rome, Italy.

Cancers
|July 27, 2022
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Summary
This summary is machine-generated.

Machine learning (ML) and artificial intelligence (AI) enhance thyroid nodule ultrasound analysis, improving diagnostic accuracy and reducing variability. These technologies offer benefits for training and in areas with limited expertise.

Keywords:
artificial intelligencemachine learningthyroid cancer

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Thyroid nodules require accurate characterization for cancer diagnosis.
  • Ultrasound is a primary imaging modality for thyroid nodules.
  • Current diagnostic methods can suffer from inter-observer variability.

Purpose of the Study:

  • To review the current state of machine learning (ML) and artificial intelligence (AI) in thyroid nodule ultrasound characterization.
  • To highlight the benefits and controversies surrounding AI implementation in this field.
  • To discuss the potential impact of AI on diagnostic accuracy and clinical workflow.

Main Methods:

  • Review of current literature on ML and AI applications in thyroid nodule ultrasound.
  • Analysis of studies demonstrating AI's impact on diagnostic accuracy and inter-observer variability.
  • Discussion of radiomics and computer-assisted diagnosis in thyroid nodule evaluation.

Main Results:

  • AI demonstrates potential to increase diagnostic accuracy in thyroid nodule characterization.
  • Standardized algorithms used by AI can significantly limit inter-observer variability.
  • AI can serve as a valuable tool in settings with limited sub-specialty expertise, offering a second opinion.

Conclusions:

  • AI represents a revolutionary advancement in thyroid nodule evaluation.
  • Key considerations for AI implementation include integration with radiologist expertise, workflow efficiency, and performance monitoring.
  • Further research is needed to address challenges and optimize AI's role in clinical practice.