Saeed Shadpour
Papers
8
Total Citations
93
H-Index
6
About
Saeed Shadpour is an emerging researcher at the forefront of surgical skill assessment and cognitive performance evaluation, with a focus on robot-assisted surgery (RAS) and laparoscopic procedures. His work centers on harnessing neurophysiological and ocular data — specifically electroencephalography (EEG) and eye-tracking — to build objective, machine learning-driven models that classify surgical expertise and predict performance outcomes. Shadpour's most significant contributions lie in developing classification frameworks that distinguish inexperienced, competent, and experienced surgeons using gradient boosting algorithms, functional brain network analysis, and multimodal sensor fusion. His research addresses a longstanding challenge in surgical education: the over-reliance on subjective, inconsistent evaluation methods. By introducing data-driven alternatives, his work has meaningful implications for surgical training standardization and patient safety. Among his notable achievements is work on predicting Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis, a procedure with direct clinical significance in prostate cancer surgery. His studies also extend to cognitive workload modeling and learning rate prediction across both RAS and FLS environments. With over 90 citations accumulated primarily within a two-year publication window, Shadpour's research is rapidly gaining recognition within the surgical informatics and biomedical engineering communities.
Research Focus
Key Achievements
Top Papers
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