Papers

17

Total Citations

263

H-Index

9

About

Riccardo Bientinesi is a urological surgeon and researcher whose work sits at the forefront of robotic and minimally invasive urology. His scholarship centers on robot-assisted radical prostatectomy, robotic partial nephrectomy, and the comparative evaluation of emerging surgical platforms. Bientinesi has played a particularly notable role in the clinical introduction and assessment of the Hugo™ RAS system by Medtronic, contributing early descriptive and outcomes-based research that has garnered over 130 citations across multiple studies and helped establish foundational benchmarks for this novel platform. His 2022 paper describing Hugo™ RAS docking settings for radical prostatectomy — though subsequently retracted — nonetheless attracted 68 citations, underscoring the field's hunger for guidance on new robotic technology. Beyond platform evaluation, Bientinesi has investigated surgical learning curves for positive margin rates following robot-assisted prostatectomy, offering practical insights into structured surgical training. His comparative work — examining robotic versus open approaches for cystectomy, pyeloplasty, and partial nephrectomy using rigorous outcome measures such as trifecta and pentafecta endpoints — demonstrates a consistent commitment to evidence-based surgical decision-making. His broader interests extend to surgeon fatigue in minimally invasive contexts, reflecting a holistic view of operative performance and patient safety.

Research Focus

Key Achievements

9
H-Index
17
Papers
263
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
RETRACTED: The new surgical robotic platform HUGO<sup>TM</sup> RAS: System description and docking settings for robot-assisted radical prostatectomy
68 citations · 2022
📈 Most Prolific Year: 2024 (7 Papers)
🤝 Key Collaborators: 72
🏛 Institutions: Università Cattolica del Sacro Cuore, Agostino Gemelli University Polyclinic

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago