Mehdi Seilanian Toussi
Papers
5
Total Citations
60
H-Index
4
About
Mehdi Seilanian Toussi is an emerging researcher at the intersection of surgical education, biomedical engineering, and artificial intelligence, with a focused expertise in objective surgical skill assessment for robot-assisted surgery (RAS) and laparoscopic procedures. His work centers on harnessing physiological and behavioral data — particularly electroencephalogram (EEG) signals and eye-tracking metrics — combined with advanced machine learning algorithms to develop reliable, data-driven models for classifying and predicting surgical expertise levels. Among his most significant contributions, Toussi has pioneered classification frameworks capable of distinguishing inexperienced, competent, and experienced surgeons using gradient boosting and other machine learning techniques, achieving meaningful benchmarks in automated skill evaluation. His research extends to predicting learning rates in fundamental laparoscopic surgery tasks and assessing critical procedural competencies such as vesico-urethral anastomosis during radical prostatectomy, directly linking objective metrics to patient outcome quality. His published studies have collectively garnered approximately 60 citations since 2023, reflecting rapid recognition within the surgical training and medical robotics communities. Toussi's investigations into eye movement behavior as an expertise biomarker further highlight his innovative approach to transforming surgical education through neuroscientific and computational methods.
Research Focus
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Top Papers
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