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Related Concept Videos

Ultrasonography01:17

Ultrasonography

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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.
During an ultrasonography procedure, a handheld device called...
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Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

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Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
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Related Experiment Video

Updated: Mar 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Ultrasound-based artificial intelligence for breast lesion classification.

Ting Ma1, Zhen Wang2, Jian Dong2

  • 1Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Oncology
|March 16, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise for breast ultrasound screening, but clinical translation faces hurdles. Rigorous validation is needed to ensure AI tools effectively improve breast cancer diagnosis in real-world settings.

Keywords:
artificial intelligencebreast lesionconvolutional neural networksdeep learningultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is the most common cancer in women, making early detection vital.
  • Breast ultrasound is a key screening tool, especially for dense breasts.
  • Current ultrasound methods face limitations due to operator dependence and interpretation variability.

Purpose of the Study:

  • To review recent advancements in artificial intelligence (AI) for breast ultrasound.
  • To critically analyze challenges in translating AI from research to clinical practice.
  • To identify methodological limitations hindering AI's real-world effectiveness.

Main Methods:

  • This narrative review synthesizes recent research on AI in breast ultrasound.
  • It analyzes technological progress, clinical translation barriers, and implementation strategies.
  • The review critically examines common methodological flaws in AI studies.

Main Results:

  • AI demonstrates potential to improve breast ultrasound accuracy and efficiency.
  • Significant gaps exist in clinical validation, particularly for generative AI and non-mass lesion diagnosis.
  • Limited multi-center data exists for commercial AI systems.

Conclusions:

  • Methodological limitations like small sample sizes and lack of external validation often overestimate AI performance.
  • Addressing these gaps is crucial for the evidence-based translation of AI into clinical practice.
  • This review provides insights for rigorous AI implementation in breast cancer diagnostics.