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Related Concept Videos

Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Updated: Dec 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep Learning in Medical Imaging.

Mingyu Kim1, Jihye Yun1, Yongwon Cho1

  • 1Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

Neurospine
|January 7, 2020
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs), a type of machine learning, have evolved significantly. Deep neural networks now excel in medical imaging tasks, overcoming earlier limitations.

Keywords:
Artificial intelligenceDeep learningMachine learningPrecision medicineRadiology

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Last Updated: Dec 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Artificial neural networks (ANNs), inspired by the brain, faced challenges like overfitting and vanishing gradients due to limited computing power and data.
  • Advancements in graphics processing units (GPUs) and large-scale data acquisition have enabled deep neural networks (DNNs) to overcome these limitations.

Purpose of the Study:

  • This review details the historical development of ANNs.
  • It explores the evolution of deep neural networks.
  • The review highlights current and potential applications of DNNs in medical imaging.

Main Methods:

  • Review of historical advancements in artificial neural network architectures.
  • Analysis of the impact of increased computing power and data availability on deep learning.
  • Survey of recent applications of deep learning in medical imaging.

Main Results:

  • Deep neural networks now surpass human capabilities in specific AI tasks like computer vision and speech recognition.
  • DNNs demonstrate significant potential in various healthcare applications, including medical image analysis.
  • Key applications include computer-aided detection/diagnosis, disease prediction, image segmentation, and image generation.

Conclusions:

  • Deep neural networks represent a major advancement over traditional ANNs, particularly in complex tasks.
  • The capabilities of DNNs are increasingly being leveraged to address critical challenges in medical imaging.
  • Continued research and development promise further transformative applications in healthcare.