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Imaging Studies III: Computed Tomography01:27

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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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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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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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The urinary bladder is a hollow, muscular sac that temporarily stores urine before it is expelled from the body. It can hold approximately 600 mL of urine prior to micturition. The bladder is retroperitoneal and located behind the pubic symphysis in the pelvic floor.
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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer

Abdallah A Mohamed1,2, Yousry AbdulAzeem3, Abdullateef I Almudaifer4

  • 1Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

A new transparent deep learning model, YOLOv11-large, accurately detects bladder cancer (urothelial cell carcinoma) from histopathology slides. This AI system offers reliable, visual diagnostic support, improving upon traditional methods.

Keywords:
You Only Look Oncebladder cancerdeep learninghistopathology diagnosis

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Area of Science:

  • * Computational pathology
  • * Artificial intelligence in oncology
  • * Medical image analysis

Background:

  • * Bladder cancer is a global health concern with high recurrence rates.
  • * Current diagnostic methods are invasive, time-consuming, and subjective.
  • * There is a need for accurate, efficient, and objective diagnostic tools.

Purpose of the Study:

  • * To develop and evaluate a transparent deep learning model for bladder cancer detection.
  • * To enhance the accuracy and efficiency of histopathology slide analysis.
  • * To provide visual support for AI-driven diagnostic predictions.

Main Methods:

  • * Training and testing five variants of the YOLOv11 deep learning architecture (nano, small, medium, large, extra large).
  • * Utilizing a dataset of hematoxylin and eosin-stained histopathology slides categorized into inflammation, urothelial cell carcinoma (UCC), and invalid tissue.
  • * Employing performance metrics including accuracy, precision, recall, AUPRC, ROC-AUC, risk-coverage analysis, and Expected Calibration Error (ECE).

Main Results:

  • * The YOLOv11-large model achieved the highest performance with 97.09% accuracy, 95.47% precision, and 95.47% recall.
  • * Detailed assessments using AUPRC, ROC-AUC, and risk-coverage analysis confirmed the model's steadiness and trustworthiness.
  • * The model demonstrated excellent calibration (low ECE) and identified potential morphological overlap between inflammation and invalid samples.

Conclusions:

  • * The YOLOv11-large model represents a significant advancement in AI-assisted bladder cancer diagnosis.
  • * The transparent nature of the model provides visual support, enhancing reliability and interpretability.
  • * This computationally efficient and scalable AI system moves closer to practical clinical application.