Samuel Mingo
Papers
2
Total Citations
66
H-Index
2
About
Samuel Mingo is a leading researcher in surgical simulation and robotic surgery outcomes, with a focus on urological oncology and skill assessment. His work centers on two key areas: predicting patient recovery after robot-assisted radical prostatectomy and optimizing robotic training methodologies. In his highly cited 2021 study (44 citations), Mingo developed a novel survival analysis model that integrates surgeon skill metrics with patient factors to predict urinary continence recovery, demonstrating that technical proficiency directly impacts functional outcomes. This work bridges the gap between surgical performance and patient-centered results. His second major contribution (22 citations) compares virtual reality and dry laboratory robotic training environments, revealing how cognitive workload and automated performance metrics differ across platforms. By establishing that skill assessment is transferable between VR and physical models, Mingo has informed the design of more effective, cost-efficient training curricula. His research has practical implications for surgical education and quality improvement, helping to standardize robotic training and improve patient recovery trajectories. Mingo’s work is essential reading for surgeons, educators, and researchers interested in evidence-based surgical training and outcome prediction.
Research Focus
Key Achievements
Top Papers
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