New AI model can predict blood loss in liposuction
New Delhi, Dec 26 (IANS/WISHAVWARTA) A newly developed artificial intelligence (AI) model is highly accurate in predicting blood loss in patients undergoing high-volume cosmetic surgery procedures, such as liposuction, according to a study.
While liposuction performed in more than 2.3 million patients per year to remove stubborn fat from the face, abdomen, thighs, arms, or neck is generally safe, excessive blood loss is a potentially serious complication, especially when higher volumes of fat are removed.
The study reported in the Plastic and Reconstructive Surgery journal called the development of an AI model to predict blood loss in liposuction a “groundbreaking advancement” with the potential to improve patient safety and surgical outcomes.
“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,” said the international research team, including those from the Department of Public Health, Health Ministry of Ecuador, and Mayo Clinic in the US.
The team used machine learning technologies to analyse data from 721 patients undergoing large-volume liposuction, with a total volume of over 4,000 milliliters (four liters) of fat and fluid removed. All procedures were carried out at two clinics, one in Colombia and one in Ecuador, following identical liposuction protocols.
Data from a random sample of 621 patients were used to develop a model for predicting estimated blood loss, incorporating a wide range of demographic, clinical, and surgical data. The researchers then tested their model’s performance in predicting the volume of blood loss in the remaining 100 patients.
With 94 per cent accuracy, the model was found to help make liposuction safer.
“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 said.
“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,” they added.
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