Jacqueline Maya-Silva
Papers
3
Total Citations
34
H-Index
2
About
Jacqueline Maya-Silva is a surgical researcher focused on advancing the assessment of technical skill in robotic surgery. Her work lies at the intersection of surgical education, objective performance measurement, and machine learning. She is best known for co-developing the Dissection Assessment for Robotic Technique (DART) tool, the first validated instrument specifically designed to evaluate the quality of surgical dissection during robotic procedures. This work, published in 2021, addresses a critical gap in surgical training and accreditation by providing an objective, reproducible scoring method. The DART tool has already garnered 17 citations, reflecting its immediate relevance to the field. In parallel, Maya-Silva has contributed to pioneering research using machine learning to analyze automated performance metrics (APMs) from robotic systems. Her 2022 study demonstrated that APMs derived from instrument kinematics can predict positive surgical margins after robot-assisted radical prostatectomy, offering a data-driven approach to improving patient outcomes. By combining rigorous tool development with computational analysis, Maya-Silva is helping to define the future of evidence-based surgical training and quality assurance.
Research Focus
Key Achievements
Top Papers
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