Mostafa Daneshgar Rahbar
Papers
3
Total Citations
20
H-Index
2
About
Mostafa Daneshgar Rahbar is a researcher at the forefront of computer vision and artificial intelligence for minimally invasive surgery. His work centers on enhancing patient safety by developing intelligent systems that can predict and detect intraoperative complications in real time. Rahbar’s major contributions include a novel entropy-based algorithm for predicting unintentional surgical-tool-induced bleeding during robotic and laparoscopic procedures, a method that analyzes abrupt instrument movements to foresee vascular injuries before they become critical. His seminal paper, “Visual Intelligence: Prediction of Unintentional Surgical-Tool-Induced Bleeding during Robotic and Laparoscopic Surgery,” has garnered 9 citations, alongside his equally cited work on detecting and localizing intraoperative bleeding using an entropy-based approach. More recently, Rahbar introduced EUGNet (Enhanced U-Net with GridMask), a deep learning architecture that improves robotic surgical tool segmentation through advanced image augmentation, achieving 2 citations since 2023. By combining real-time monitoring with cutting-edge AI, Rahbar’s research directly addresses life-threatening vascular injuries, offering a pathway to safer, more autonomous surgical systems. His work is essential reading for anyone interested in the intersection of machine learning, image processing, and surgical robotics.
Research Focus
Key Achievements
Top Papers
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