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Related Concept Videos

Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Virtual multi-staining in a single-section view for renal pathology using generative adversarial networks.

Masataka Kawai1, Toru Odate1, Kazunari Kasai1

  • 1Department of Pathology, University of Yamanashi, Chuo, Yamanashi, Japan.

Computers in Biology and Medicine
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence using PPHM-GAN can virtually re-stain renal biopsy images. This AI tool enhances the recognition of kidney diseases by providing multiple stain views from a single slide.

Keywords:
Artificial intelligenceGANRenal biopsyRenal pathologyVirtual staining

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Area of Science:

  • Nephrology
  • Computational Pathology
  • Artificial Intelligence

Background:

  • Renal biopsy interpretation relies on multiple tissue stains like PAS, PAM, H&E, and MT.
  • Pathological diagnosis can be challenging due to morphological variations between stains.

Purpose of the Study:

  • To develop and validate an AI-based system (PPHM-GAN) for virtual multi-stain transformation in renal pathology.
  • To assess the diagnostic utility of AI-transformed stains for identifying renal abnormalities.

Main Methods:

  • Trained generative adversarial networks (GANs) for stain transformation between PAS, PAM, H&E, and MT.
  • Evaluated transformation quality using human assessment and quantitative metrics (FID, PSNR, SSIM, CSSIM, DSIS).
  • Validated diagnostic performance on identifying glomerular and interstitial abnormalities in renal biopsy images.

Main Results:

  • PPHM-GAN demonstrated effective multi-stain to multi-stain transformation capabilities.
  • Transformed stains sometimes improved the recognition of crescent formation, mesangial hypercellularity, sclerosis, and interstitial lesions.
  • AI-transformed stains, particularly to PAM and from H&E, significantly enhanced crescent formation detection (p < 5.0E-9).

Conclusions:

  • PPHM-GAN maximizes information extraction from single renal biopsy sections by simulating multiple stains.
  • This AI approach offers a novel strategy for renal biopsy staining and diagnostic interpretation.
  • Virtual re-staining has the potential to improve diagnostic accuracy and efficiency in renal pathology.