A newly developed artificial intelligence model may improve safety and surgical planning for high-volume liposuction by accurately forecasting intraoperative blood loss, according to research published in the January 2026 issue of Plastic and Reconstructive Surgery, the official journal of the American Society of Plastic Surgeons.
The multicentre study, led by Dr Mauricio E Perez Pachon of the Mayo Clinic (Rochester, Minnesota) and Dr Jose T Santaella of CIMA Clinic-Loja (Ecuador), applied machine learning techniques to detailed clinical, demographic and procedural data from 721 patients who underwent large-volume liposuction.
The research team used data from 621 patients to train the model and then tested its performance in a separate set of 100 patients. The AI tool analysed a wide range of variables – from patient characteristics to specifics of the surgical procedure – and generated predictions of blood loss for individual cases.
Using AI to help predict blood loss during liposuction
Results showed “excellent agreement” between predicted and actual blood loss, with the model achieving approximately 94 percent accuracy. Variation between predicted and observed blood loss was low, with a standard deviation of 26 mL and a maximum discrepancy of 188 mL.
‘Such accuracy reinforces the model’s potential as a decision-support tool in body contouring procedures, where anticipating intraoperative blood loss is crucial for patient safety and operative planning,’ the researchers write. ‘Surgeons can use the predicted blood loss estimates to make informed decisions about perioperative management, such as the need for blood transfusions, fluid management, and other critical care measures.’
Implications for surgical planning and patient safety
Liposuction remains the most frequently performed cosmetic surgical procedure globally, with more than 2.3 million cases annually. Although generally classed as safe, excessive blood loss can pose significant risks, particularly in high-volume body contouring. Accurate preoperative prediction of blood loss could support more informed decision-making around perioperative fluid management, transfusion planning and overall operative strategy.
The researchers describe the AI tool as a potential ‘decision-support’ asset that could help surgeons tailor interventions to individual patients.
‘Developing and implementing our AI model for predicting blood loss in liposuction is a groundbreaking advancement that promises to improve patient safety and surgical outcomes,’ the authors state. ‘By leveraging the power of AI-driven predictive models, surgeons can tailor their interventions to each patient’s unique needs, ensuring optimal outcomes and minimising the risk of complications such as excessive blood loss.’
While further validation in diverse populations and settings is needed, the team plans to expand training of the algorithm using data from additional surgical centres worldwide, with the aim of refining its predictive capability and broadening its applicability.
