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Related Concept Videos

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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

189
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...
189
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

119
Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
119

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Related Experiment Video

Updated: Dec 25, 2025

Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys
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Artificial intelligence driven next-generation renal histomorphometry.

Briana A Santo1, Avi Z Rosenberg, Pinaki Sarder

  • 1Department of Pathology and Anatomical Sciences, The State University of New York, Buffalo, New York Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Current Opinion in Nephrology and Hypertension
|March 25, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in renal pathology uses machine learning and artificial neural networks (ANNs) for kidney morphology analysis. This enables automated diagnostics and prognostics, with ongoing work to overcome challenges for precision medicine.

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Area of Science:

  • Computational pathology
  • Artificial intelligence in medicine
  • Renal pathology informatics

Background:

  • Early digital pathology used classical image analysis and machine learning for renal disease prognostication.
  • Advancements in hardware have led to the adoption of artificial neural networks (ANNs) for machine vision in computational pathology.

Purpose of the Study:

  • To discuss the role of AI, specifically image analysis and machine learning, in characterizing kidney morphology.
  • To highlight the development of automated diagnostic and prognostic applications in renal pathology.

Main Methods:

  • Utilizing artificial neural networks (ANNs) for machine vision in computational pathology.
  • Applying machine learning for the rapid and reproducible detection, characterization, and classification of kidney morphology.

Main Results:

  • ANNs facilitate the development of diagnostic and prognostic applications in renal pathology.
  • Modern machine learning has identified novel biomarkers in kidney disease, suggesting potential for discovering new pathologic mechanisms.

Conclusions:

  • Successful AI integration requires clinician comprehension and addressing challenges like data quality, annotation, and interpretability.
  • Overcoming these challenges will revolutionize diagnostic pathology, enable precision medicine, and integrate AI into patient care.