Pierpaolo Di Cocco
Papers
2
Total Citations
6
H-Index
2
About
Pierpaolo Di Cocco is an emerging surgical researcher whose work sits at the dynamic intersection of robotic surgery, transplantation medicine, and artificial intelligence-driven clinical decision-making. His research focuses primarily on optimizing kidney transplantation outcomes, with particular emphasis on robotic-assisted kidney transplantation (RAKT) as a viable alternative to conventional open procedures, especially for complex patient populations such as those with morbid obesity. Among his most notable contributions is a 2024 study analyzing cold ischemia time (CIT) impact across what is reported as the largest cohort of robotic kidney transplants from deceased donors — a landmark dataset that advances understanding of graft outcomes in this technically demanding setting. Equally significant is his pioneering application of machine learning to transplant surgery: his decision tree model, trained on nearly 1,000 patients, provides clinicians with a data-driven framework for choosing between open and robotic surgical approaches, representing a meaningful step toward precision surgical planning. Both papers have garnered 3 citations each since their 2024 publication, reflecting rapid early engagement from the transplant and surgical communities. Di Cocco's work positions him as a forward-thinking contributor helping to reshape how transplant surgery is practiced and personalized in the modern era.
Research Focus
Key Achievements
Top Papers
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