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Related Concept Videos

Assessment of the Cardiovascular System II: Inspection01:29

Assessment of the Cardiovascular System II: Inspection

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Inspection is the initial step in assessing the cardiovascular system. It involves a detailed visual examination that provides crucial information about a patient's circulatory and cardiac health. This systematic process, conducted from head to toe, helps identify signs of cardiovascular conditions by observing physical appearance, skin and mucous membranes, jugular and carotid pulsations, chest symmetry, and the condition of the extremities.
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The abdominal examination is a cornerstone of clinical medicine, serving as a critical tool in diagnosing various gastrointestinal (GI) diseases. It involves a systematic approach that includes inspection and auscultation, each with distinct yet complementary roles in assessing the abdomen. This article will delve into these two primary methods healthcare professionals use to examine the abdomen.
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Related Experiment Video

Updated: Feb 2, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
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Computerized Ultrasonic Imaging Inspection: From Shallow to Deep Learning.

Jiaxing Ye1, Shunya Ito2, Nobuyuki Toyama3

  • 1National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan. jiaxing.you@aist.go.jp.

Sensors (Basel, Switzerland)
|November 9, 2018
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Summary
This summary is machine-generated.

This study introduces an automated system for ultrasonic image analysis in nondestructive testing (NDT). Deep learning methods significantly outperformed traditional techniques for defect detection, offering a more reliable alternative to manual inspection.

Keywords:
computer visionconvolutional neural networksdeep learninglocal descriptornondestructive evaluationultrasonic imaging

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Area of Science:

  • Materials Science and Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Ultrasonic imaging inspection is a primary method for detecting defects like voids and corrosion.
  • Manual interpretation of ultrasonic data is subjective and prone to human error.
  • Advanced computer vision offers potential for automated visual analysis.

Purpose of the Study:

  • To develop an efficient automatic ultrasonic image analysis system for nondestructive testing (NDT).
  • To leverage the latest visual information processing techniques for defect detection.
  • To benchmark conventional and deep learning methods for ultrasonic image analysis.

Main Methods:

  • Established a dataset of 6849 annotated ultrasonic scan images.
  • Conducted a comparative analysis of conventional (shallow) learning and convolutional neural networks (CNNs) for defect detection.
  • Utilized representation learning through multi-layer stacking in CNNs.

Main Results:

  • Deep learning-enabled systems demonstrated superior performance compared to conventional shallow learning methods.
  • The automated system achieved efficient and accurate defect detection in ultrasonic images.
  • Significant improvements in defect identification accuracy were observed with deep learning approaches.

Conclusions:

  • Deep learning significantly enhances the accuracy and efficiency of automatic defect detection in ultrasonic imaging.
  • The developed system provides a reliable, automated alternative to subjective manual inspection in NDT.
  • This research serves as a valuable reference for future studies in automated ultrasonic defect detection.