Predicting Resection Weights of Reduction Mammaplasty: A Multi-Institutional Retrospective Analysis Using Machine Learning
ML and regression modeling demonstrated improved accuracy in predicting resection weights for RM compared with the Schnur Scale in a heterogenous and multiinstitutional population. This study provides further evidence of promising alternatives to the Schnur Scale.
Authors
- Brett K. Beaulieu‐Jones (ORCID: https://orcid.org/0000-0002-6700-1468)
- Linda G. Phillips
- Stefanos Boukovalas (ORCID: https://orcid.org/0000-0002-2131-0046)
- Devin John Clegg (ORCID: https://orcid.org/0000-0002-5454-114X)
- Gulsah Onar (ORCID: https://orcid.org/0000-0001-8507-7426)
- George Kokosis (ORCID: https://orcid.org/0000-0002-9531-615X)
- Michelle Seu (ORCID: https://orcid.org/0000-0002-6122-4767)
- Kimberly H. Khoo (ORCID: https://orcid.org/0000-0001-5935-8179)
- Kimberley C. Brondeel (ORCID: https://orcid.org/0000-0002-5075-8747)
- Aaron N. Hendizadeh
Institutions
- Rush University Medical Center (US)
- Loyola University Medical Center (US)
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Knoxville College (US)
- University of Chicago (US)
- Center For Reconstructive Urethral Surgery (IT)
- University of Tennessee at Knoxville (US)
Publication Details
- Journal
- Plastic & Reconstructive Surgery
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1097/prs.0000000000012258
- Citations
- 1
- Primary Topic
- Breast Implant and Reconstruction
- Type
- article
- Field-Weighted Citation Impact
- 2.01