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Related Concept Videos

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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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...
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

Imaging Studies V: Intravenous Urography and Retrograde Pyelography

IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...

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Related Experiment Video

Updated: May 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Artificial Intelligence in Renal Imaging: A Multi-Dataset Study for Kidney Disease Classification.

Berçem Afşar Karatepe1, Burak Tasci2

  • 1Internal Medicine Department, Elazığ Fethi Sekin City Hospital, 23280 Elazig, Türkiye.

Biomedicines
|May 27, 2026
PubMed
Summary

A new Hybrid Multi-Path Attention Convolutional Neural Network (HMPA-CNN) accurately classifies kidney diseases across diverse datasets and imaging types. This AI tool shows promise for renal imaging support, aiding in disease screening and diagnosis.

Keywords:
attention mechanismscross-institutional validationdeep learningdual-path neural networkskidney disease classificationmedical imaging

Related Experiment Videos

Last Updated: May 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning for Radiology
  • Computational Pathology

Background:

  • Accurate kidney disease classification is crucial for patient outcomes.
  • Heterogeneous datasets and imaging modalities pose challenges for AI models.
  • Existing AI models may struggle with diverse pathological presentations and imaging techniques.

Purpose of the Study:

  • To develop and evaluate a Hybrid Multi-Path Attention Convolutional Neural Network (HMPA-CNN).
  • To assess the HMPA-CNN's performance in classifying kidney diseases across multiple institutions and imaging modalities (CT and MRI).
  • To investigate the model's efficacy in various tasks, including tumor discrimination, pathology classification, subtyping, kidney stone detection, and chronic kidney disease assessment.

Main Methods:

  • The HMPA-CNN utilizes dual parallel pathways for spatial and textural feature extraction.
  • Attention mechanisms and gated fusion are employed for feature recalibration.
  • The model was trained and validated on 29,148 images from five distinct international datasets.
  • Performance was evaluated across multiple classification tasks with varying diagnostic granularity.

Main Results:

  • The HMPA-CNN achieved high accuracies, including 99.76% for three-class tumor discrimination and 99.96% for four-class renal pathology.
  • The model demonstrated strong performance in six-class tumor subtyping (96.36%) and chronic kidney disease classification (93.85% on MRI).
  • Specificity exceeded 99% in most tasks, with benign-malignant misclassification below 2%; kidney stone detection accuracy was 91.96%.

Conclusions:

  • The HMPA-CNN demonstrates robust and consistent performance across diverse datasets, institutions, and imaging modalities.
  • Its high specificity and low benign-malignant misclassification rate suggest utility as a decision-support tool in renal imaging.
  • The model should be used as an assistive tool, not an autonomous diagnostic system, requiring further prospective validation.