Papers
19
Total Citations
489
H-Index
10
About
Saum Ghodoussipour is a urologic surgeon and researcher whose work sits at the intersection of robotic surgery, surgical performance assessment, and machine learning-driven outcomes prediction. He has made particularly influential contributions to the field of robot-assisted radical prostatectomy (RARP), pioneering the use of automated performance metrics (APMs) to objectively quantify and differentiate surgeon skill levels — work that has collectively garnered hundreds of citations. His landmark 2019 deep-learning study (144 citations) demonstrated that combining APMs with clinical features could accurately predict urinary continence recovery after RARP, offering a powerful tool for surgical quality improvement. Ghodoussipour has also explored how surgeon experience, pelvic anatomy, and trainee involvement shape both technical performance and patient outcomes, while extending APM validation to robotic partial nephrectomy. His research on single-port robotic prostatectomy and robotic radical cystectomy — including readmission patterns and ureteroenteric stricture repair — reflects a broad commitment to advancing minimally invasive urologic oncology. By bridging objective surgical assessment with clinical outcome data, Ghodoussipour's body of work is shaping how surgeons are trained, evaluated, and held accountable in the robotic era.
Research Focus
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