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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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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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The diagnosis of renal calculi involves several imaging techniques, including non-contrast CT scans and ultrasound. These methods help visualize kidney stones, assess their size and location, and detect possible obstructions. Additionally, Measuring urine pH is useful for diagnosing specific stone types, such as struvite (alkaline pH) and uric acid stones (acidic pH). Cystine stones are primarily linked to cystinuria, a genetic condition. A urinalysis helps detect blood in the urine (hematuria)...
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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...
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The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.
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Management of renal calculi focuses on effective strategies like tailored nutrition and hydration therapy. Adjusting diet and fluid intake reduces stone formation and recurrence, making these interventions simple yet powerful in kidney stone prevention and management.Understanding Kidney StonesKidney stones form when calcium, oxalate, uric acid, and cystine concentrate and crystallize in urine. Factors contributing to their formation include genetic predisposition, certain medical conditions,...
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Deep learning model for automated kidney stone detection using coronal CT images.

Kadir Yildirim1, Pinar Gundogan Bozdag2, Muhammed Talo3

  • 1Department of Urology, Faculty of Medicine, University of Turgut Ozal, Malatya, Turkey.

Computers in Biology and Medicine
|June 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for automated kidney stone detection using CT images. The AI model achieved 96.82% accuracy, aiding in early diagnosis and treatment.

Keywords:
Computed tomographyDeep learningKidney stoneMedical image

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Area of Science:

  • Urology
  • Radiology
  • Artificial Intelligence

Background:

  • Kidney stones are a prevalent global health issue causing severe pain and emergency room visits.
  • Accurate diagnosis of kidney stones relies on medical imaging interpretation, often requiring specialist expertise.
  • Computer-aided diagnosis (CADx) systems offer potential as auxiliary tools to support clinical decision-making.

Purpose of the Study:

  • To develop and evaluate an automated deep learning (DL) model for detecting kidney stones in coronal computed tomography (CT) images.
  • To assess the accuracy and clinical applicability of the DL model in identifying kidney stones, including small ones.

Main Methods:

  • A deep learning model was trained and tested using a dataset of 1799 coronal CT images from 433 subjects.
  • The model was designed for automated binary classification: presence or absence of kidney stones.

Main Results:

  • The developed automated model achieved a high accuracy of 96.82% in detecting kidney stones from CT images.
  • The model demonstrated proficiency in accurately identifying even small-sized kidney stones.
  • The DL model performed well on a substantial dataset, indicating readiness for clinical evaluation.

Conclusions:

  • Deep learning techniques show significant promise for addressing complex challenges in urological imaging analysis.
  • The developed automated kidney stone detection system can serve as a valuable tool in clinical practice.
  • This AI-driven approach has the potential to improve the efficiency and accuracy of kidney stone diagnosis.