Breast Parenchimal EnhancementArtificial Intelligence (AI)
Study summary
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.
Eligibility
Sex
FEMALE
Min age
18 Years
Max age
—
Healthy volunteers
No
Patients who underwent CEM, mammography, and ultrasound between May 2022 and June 2023.
Availability of BPE assessment, BI-RADS density classification, and age data.
Complete dataset available for statistical and AI-based analysis.
Exclusion Criteria:
Patients with prior breast cancer treatment that could alter BPE.
Incomplete imaging or missing classification data.
Contraindications to contrast-enhanced imaging.
Primary outcome measure(s)
Correlation between breast density, BPE, and age using AI-driven analysis. — Data analysis within 12 months of study completion. Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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