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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.
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Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
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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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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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Artificial intelligence in ultrasound.

Yu-Ting Shen1, Liang Chen2, Wen-Wen Yue1

  • 1Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Tongji University Cancer Center, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, National Clnical Research Center of Interventional Medicine, Shanghai, 200072, PR China.

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|May 7, 2021
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Summary

Artificial intelligence (AI) enhances ultrasound (US) diagnostics by overcoming operator dependence. AI, machine learning (ML), and deep learning (DL) offer improved accuracy and reproducibility in medical imaging analysis.

Keywords:
Artificial intelligenceDeep learningMedical imagingUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Ultrasound Technology

Background:

  • Ultrasound (US) is a flexible, green imaging modality increasingly used globally.
  • Current US practice relies on manual image evaluation by physicians, leading to operator-dependent diagnostic variability.
  • Advanced ultrasonic technologies and digital health systems are driving US expansion.

Purpose of the Study:

  • To provide an overview of AI, machine learning (ML), and deep learning (DL).
  • To review the applications of AI, particularly DL, in various anatomical regions using US.
  • To discuss the challenges and opportunities for AI in clinical US practice.

Main Methods:

  • Review of AI, ML, and DL concepts.
  • Survey of AI applications in US across thyroid, breast, abdomen, pelvis, obstetrics, heart, blood vessels, and musculoskeletal systems.
  • Analysis of AI roles in image quality control, localization, detection, segmentation, and diagnosis.

Main Results:

  • AI excels at pattern recognition and quantitative assessment, addressing US operator dependence.
  • AI applications in US cover image quality, anatomical localization, object detection, lesion segmentation, and computer-aided diagnosis.
  • Deep learning shows significant potential in enhancing diagnostic accuracy and reproducibility in US.

Conclusions:

  • AI, especially DL, holds substantial promise for improving the accuracy and reproducibility of ultrasound diagnostics.
  • Addressing challenges and leveraging opportunities is crucial for the clinical integration of AI in US.
  • AI can assist physicians in achieving more precise and consistent diagnostic outcomes in medical imaging.