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| Name | Class |
|---|---|
| University of Campania Luigi Vanvitelli | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| patiens underwent CEM |
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| Measure | Description | Time Frame |
|---|---|---|
| Correlation between breast density, BPE, and age using AI-driven analysis. | Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression. | Data analysis within 12 months of study completion. |
| Measure | Description | Time Frame |
|---|---|---|
| AI-based optimization of breast density and BPE classification | Evaluating the performance of neural networks in predicting BPE levels across different breast density categories. | Within 12 months of study completion |
| Comparative performance of multiple linear regression vs. AI models. |
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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.
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Women aged 18 years and older who underwent CEM for diagnostic or surveillance purposes.
Patients with recorded BPE levels and BI-RADS breast density classification.
Relational database containing structured data for correlation matrix analysis and AI model training.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Campania Luigi Vanvitelli | Naples | 80138 | Italy |
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Assessing the accuracy of traditional statistical methods versus ANN-based predictions in explaining variance in BPE values. |
| Within 12 months of study completion. |
| Mean Squared Error (MSE) and explained variance in predictive models | Analyzing the error rates and variance explained by different AI models compared to multiple linear regression. | Within 12 months of study completion |