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Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

Michelle Y Seu, Nikki Rezania¹, Carolyn E Murray¹

¹From the Division of Plastic & Reconstructive Surgery, Rush University Medical Center, Chicago, IL.

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Summary

Machine learning models accurately predict breast resection weight using anthropometric data, offering a promising alternative to the Schnur Scale for reduction mammaplasty consultations.

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Area of Science:

Medical applications of artificial intelligence
Predictive modeling in plastic surgery

Background:

Machine learning (ML) enhances predictive accuracy in medicine.
This study aimed to develop an ML model for predicting breast resection weight using anthropometric measurements.

Purpose of the Study:

To create a predictive model for breast resection weight.
To compare ML algorithm performance against the Schnur Scale.

Main Methods:

Analyzed 237 patients undergoing reduction mammaplasty.
Utilized anthropometric variables: BSA, BMI, SN-N, nipple-to-inframammary fold.
Trained and tested four ML algorithms (linear regression, ridge regression, support vector regression, random forest regression) and the Schnur Scale, evaluating accuracy via Mean Absolute Error (MAE).

Main Results:

Sternal notch-to-nipple (SN-N) distance showed the highest variable importance.
All ML models outperformed the Schnur Scale in predicting resection weight (lower MAE).
The random forest regression model without Schnur Scale data achieved the lowest MAE (186.20).

Conclusions:

ML-based prediction models offer an accurate alternative to the Schnur Scale.
This approach shows promise for improving accuracy in reduction mammaplasty consultations.