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Related Concept Videos

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  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Machine Learning Models For Volume And Weight Estimation In Breast Reconstruction Planning.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Machine Learning Models For Volume And Weight Estimation In Breast Reconstruction Planning.

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Machine learning models for volume and weight estimation in breast reconstruction planning.

Sheng-Pu Teo1, Mee-Hoong See2,3, Lee-Lee Lai4

  • 1Faculty of Computing and Informatics, Multimedia University, Selangor, Malaysia.

Health Information Science and Systems
|January 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning accurately estimates breast volume and weight using patient data, offering a cost-effective alternative for reconstruction planning. This method simplifies preoperative assessment, improving accessibility and efficiency in clinical practice.

Keywords:
BreastEstimationMachine learningReconstruction

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Area of Science:

  • Biomedical Engineering
  • Machine Learning Applications
  • Medical Imaging and Data Analysis

Background:

  • Accurate breast volume and weight estimation is crucial for post-mastectomy reconstruction.
  • Current methods often involve high costs or complex procedures.
  • A novel machine learning framework was developed to address these limitations.

Purpose of the Study:

  • To develop and validate a machine learning framework for accurate breast volume and weight estimation.
  • To leverage demographic and anthropometric data for preoperative breast assessment.
  • To provide a cost-effective and accessible alternative to existing methods.

Main Methods:

  • Data from 199 patients (2021-2023) were collected and pre-processed.
  • Feature selection utilized domain expertise, Spearman's rank correlation, and the Boruta algorithm.
Volume
  • Linear regression, random forest regression, and support vector regression models were trained and evaluated using R² and Pearson's correlation coefficient.

    • Main Results:

    • Significant correlations were found between breast volume/weight and patient characteristics like BMI, cup size, and ptosis severity.
    • The optimal linear regression model achieved an R² of 81.8% for breast volume and 72% for breast weight.
    • The model integrated domain-expert and statistically selected features.

    Conclusions:

    • Machine learning integrating demographic and anthropometric data provides an accurate, interpretable, and accessible preoperative breast assessment method.
    • This approach eliminates imaging costs and reliance on specialized equipment, utilizing routinely collected clinical data.
    • The proposed model offers a practical, efficient, and cost-effective solution for clinical practice, overcoming traditional method limitations.