首页|BG-University Hospital Reports Findings in Artificial Intelligence (Implementati on of a Machine Learning Approach Evaluating Risk Factors for Complications afte r Single-Stage Augmentation Mastopexy)
BG-University Hospital Reports Findings in Artificial Intelligence (Implementati on of a Machine Learning Approach Evaluating Risk Factors for Complications afte r Single-Stage Augmentation Mastopexy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Bochum, Germany , by NewsRx editors, research stated, "Single-stage mastopexy augmentation is a much-debated intervention due to its complexity and the associated relatively hi gh complication rates. This study aimed to reevaluate the risk factors for these complications using a novel approach based on artificial intelligence and to de monstrate its possible limitations." Our news journalists obtained a quote from the research from BG-University Hospi tal, "Complete datasets of patients who underwent single-staged augmentation mas topexy during 2014-2023 at one institution by a single surgeon were collected re trospectively. These were subsequently processed and analyzed by CART, RF and XG Boost algorithms. A total of 342 patients were included in the study, of which 4 3 (12.57%) reported surgery-associated complications, whereby capsu lar contracture (n = 19) was the most common. BMI represented the most important variable for the development of complications (FIS = 0.44 in CART). 2.9% of the patients expressed the desire for implant change in the course, with abse nce of any complications. A statistically significant correlation between smokin g and the desire for implant change (p <0.001) was reveale d. The importance of implementing artificial intelligence into clinical research could be underpinned by this study, as risk variables can be reclassified based on factors previously considered less or even irrelevant. Thereby we encountere d limitations using ML approaches. Further studies will be needed to investigate the association between smoking, BMI and the current implant size with the desi re for implant change without any complications. Moreover, we could show that th e procedure can be performed safely without high risk of developing major compli cations. This journal requires that authors assign a level of evidence to each a rticle."
BochumGermanyEuropeArtificial Inte lligenceCyborgsEmerging TechnologiesMachine LearningRisk and PreventionSurgery
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