A Machine Learning Approach to Predicting Donor Site Complications Following DIEP Flap Harvest.

Hao HuangMarcos Lu WangYunchan ChenTara M ChadabNicholas A VerniceDavid M Otterburn

Published in: Journal of reconstructive microsurgery (2023)

This study demonstrates that body mass index is superior to radiographic features of obesity in predicting donor site complications following DIEP flap harvest. Other predictors include older age and longer surgery duration. Our logistic regression machine learning model has the potential to quantify risk of donor site complications.

Keyphrases

machine learning
breast reconstruction
body mass index
risk factors
weight gain
artificial intelligence
physical activity
type diabetes
big data
minimally invasive
metabolic syndrome
adipose tissue
risk assessment
coronary artery disease
middle aged
high fat diet induced