Amir Ashraf‐Ganjouei
Papers
4
Total Citations
19
H-Index
3
About
Amir Ashraf‐Ganjouei is a surgical outcomes researcher whose work sits at the intersection of data science and minimally invasive surgery. His primary research areas include machine learning in surgical risk prediction, the adoption of minimally invasive techniques, and novel robotic-assisted procedures for gastrointestinal tumors. His most impactful contribution is a machine learning model that predicts “textbook outcome” in colectomy—a more holistic metric than traditional complication-based calculators—which has already garnered 7 citations and offers a practical tool for shared decision-making with high-risk patients. He has also analyzed national trends in minimally invasive distal pancreatectomy, showing that despite growing adoption, the U.S. has not yet made it the standard of care (6 citations). On the technical side, Ashraf‐Ganjouei has pioneered robotic-assisted endoluminal resections for benign gastroesophageal junction tumors, describing novel techniques for leiomyoma removal with transoral specimen extraction. These case series (4 and 2 citations) demonstrate his commitment to advancing both the art and science of surgery—combining rigorous outcomes analysis with innovative, patient-centered technique development.
Research Focus
Key Achievements
Top Papers
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