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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

Haoran Dai1, Yuyao Xiao1, Caixia Fu2

  • 1Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Journal of Magnetic Resonance Imaging : JMRI
|June 3, 2024
PubMed
Summary
This summary is machine-generated.

Deep learning artificial intelligence (AI) software shows practical value in automatically identifying and measuring focal liver lesions (FLLs) on MRI. The AI combined with radiologists improved detection rates and sensitivity, especially for smaller lesions.

Keywords:
automatic detectionfocal liver lesionsmagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Liver Lesion Detection

Background:

  • Increasing global detection of focal liver lesions (FLLs) necessitates robust automated detection systems.
  • Contrast-enhanced magnetic resonance imaging (MRI) is crucial for FLL characterization.

Purpose of the Study:

  • To evaluate the performance of deep learning-based artificial intelligence (AI) software for identifying and measuring FLLs on contrast-enhanced MRI.
  • To compare the diagnostic capabilities of AI, radiologists, and their combination.

Main Methods:

  • Retrospective analysis of 395 patients with 1149 FLLs using 1.5T and 3T MRI scanners.
  • AI software performance assessed for lesion segmentation (Dice coefficient) and detection sensitivity across various lesion sizes.
  • Comparison of AI and radiologist measurements against pathological sizes for 122 resected lesions.

Main Results:

  • AI achieved an average Dice coefficient of 0.62 for liver lesion segmentation.
  • The AI-radiologist combination demonstrated superior detection rates (0.894) and sensitivity (0.883) compared to radiologists alone (0.825, 0.806).
  • AI showed higher sensitivity for lesions <20 mm (0.848 vs. 0.788), while both performed similarly for lesions ≥20 mm.

Conclusions:

  • Deep learning AI software demonstrates practical utility in the automated identification and measurement of liver lesions.
  • AI significantly enhances the diagnostic performance of radiologists, particularly for smaller FLLs.
  • The AI system shows reliable agreement with radiologist measurements and pathological findings.