Abelardo Escoto

Lawson Health Research Institute, London Health Sciences Centre

Papers

Total Citations

H-Index

About

Abelardo Escoto is a researcher whose work sits at the critical intersection of robotic surgery, medical training, and patient selection. His primary research areas include the application of robotic-assisted surgical systems, particularly for coronary artery bypass grafting, and the optimization of surgical skills acquisition through mastery learning. Escoto’s most notable contribution is the development of an anatomy-based eligibility measure for robotic-assisted bypass surgery, a framework that helps clinicians determine which patients are best suited for minimally invasive procedures using the da Vinci Surgical System. This work, published in 2014, has accumulated 20 citations, reflecting its practical value in preoperative planning. In a related study, Escoto investigated how different learning methods—specifically blocked versus random practice conditions—affect skill acquisition in robotic surgery. His findings, published in 2017, offer evidence-based insights for designing more effective surgical training curricula. By addressing both the technical and educational dimensions of robotic surgery, Escoto has helped bridge the gap between emerging technology and clinical application, making his research essential reading for surgeons, medical educators, and biomedical engineers alike.