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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Introduction:
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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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Artificial intelligence in breast ultrasonography.

Jaeil Kim1, Hye Jung Kim2, Chanho Kim1

  • 1School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea.

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|January 12, 2021
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Summary
This summary is machine-generated.

Artificial intelligence (AI) can enhance breast ultrasonography by improving mass detection, diagnosis, and prediction, addressing limitations like false positives and variability. This technology shows promise as a valuable second opinion tool in clinical practice.

Keywords:
Artificial intelligenceBreast diseasesBreast neoplasmConvolutional neural networkUltrasonography

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Area of Science:

  • Radiology and Medical Imaging
  • Computer Science and Artificial Intelligence
  • Oncology and Breast Health

Background:

  • Breast ultrasonography is crucial for differentiating breast masses but faces challenges with false positives and interobserver variability.
  • Artificial intelligence (AI), especially deep learning, offers potential solutions to enhance diagnostic accuracy and workflow efficiency.

Purpose of the Study:

  • To provide a comprehensive overview of current AI applications in breast ultrasonography.
  • To discuss methodological considerations for developing AI models in this field.
  • To review the latest literature on AI's clinical potential in breast imaging.

Main Methods:

  • Review of current literature on AI in breast ultrasonography.
  • Discussion of AI model development considerations.
  • Analysis of AI applications in detection, differential diagnosis, and prognostication.

Main Results:

  • AI demonstrates significant utility in key breast ultrasonography tasks: detection (segmentation/localization), differential diagnosis (classification), and prognostication (prediction).
  • Deep learning models are central to advancements in AI for breast imaging.
  • AI is poised to improve workflow efficiency and act as a crucial second opinion.

Conclusions:

  • AI holds substantial promise for improving the accuracy and efficiency of breast ultrasonography.
  • Further research and development are needed to fully integrate AI into clinical practice for breast mass evaluation.
  • AI applications can potentially reduce diagnostic errors and enhance patient care in breast imaging.