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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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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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Related Experiment Video

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Beyond Cancer Detection: An AI Framework for Multidimensional Risk Profiling on Contrast-Enhanced Mammography.

Graziella Di Grezia1, Antonio Nazzaro2, Elisa Cisternino3

  • 1Department of Life Sciences, Health and Healthcare Professions, Link Campus University, Via del Casale di S. Pio V, 44, 00165 Rome, Italy.

Diagnostics (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

AI models enhance breast density and background parenchymal enhancement classification reproducibility using contrast-enhanced mammography. This technology also shows potential for systemic risk profiling.

Keywords:
background parenchymal enhancementbreast densitycontrast-enhanced mammographydeep learninginterobserver variabilitymulti-task learningpreventive imaging

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Quantitative Medical Imaging

Background:

  • Accurate breast density (BD) and background parenchymal enhancement (BPE) classification is crucial for mammography interpretation.
  • Contrast-enhanced mammography (CEM) offers a platform for detailed breast tissue analysis.
  • Exploring AI for improved classification consistency and potential systemic risk surrogates is an active research area.

Purpose of the Study:

  • To evaluate AI model efficacy in improving the reproducibility of breast density (BD) and background parenchymal enhancement (BPE) classification.
  • To assess if contrast-enhanced mammography (CEM) can be utilized as a proof-of-concept for systemic risk surrogate detection.
  • To compare AI-driven classification against traditional radiologist assessments.

Main Methods:

  • Retrospective analysis of 213 women undergoing CEM, with independent grading by five radiologists.
  • Comparison of linear regression and deep neural network (DNN) models against a baseline for BD/BPE classification.
  • Inter-reader agreement measured using Fleiss' kappa; external validation on 500 cases; exploratory analysis of systemic risk surrogates.

Main Results:

  • AI support improved inter-reader agreement for BD (κ=0.82) and BPE (κ=0.82) compared to baseline (κ=0.68 and κ=0.54).
  • AI models reduced prediction error by up to 26% and decreased false positives by 22%, while shortening reading time by 35%.
  • Exploratory analysis showed strong correlations between AI-derived surrogates and bone mineral density (r=0.82) and systolic blood pressure (r=0.76).

Conclusions:

  • AI significantly enhances the reproducibility and efficiency of BD/BPE assessments in CEM.
  • CEM, augmented by AI, shows feasibility for systemic risk profiling, potentially integrating imaging with broader health indicators.
  • AI-driven analysis in CEM represents a promising advancement for both diagnostic accuracy and risk stratification.