AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06838130

Enrollment

213

Registered

2025-02-20

Start date

2022-05-01

Completion date

2025-02-28

Last updated

2025-02-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence (AI), Breast Parenchimal Enhancement

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

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.

Design outcomes

Primary

Measure	Time frame	Description
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.

Secondary

Measure	Time frame	Description
AI-based optimization of breast density and BPE classification	Within 12 months of study completion	Evaluating the performance of neural networks in predicting BPE levels across different breast density categories.
Comparative performance of multiple linear regression vs. AI models.	Within 12 months of study completion.	Assessing the accuracy of traditional statistical methods versus ANN-based predictions in explaining variance in BPE values.
Mean Squared Error (MSE) and explained variance in predictive models	Within 12 months of study completion	Analyzing the error rates and variance explained by different AI models compared to multiple linear regression.

Countries

Italy

Outcome results