Danilo Pereira
Papers
1
Total Citations
11
H-Index
1
About
Danilo Pereira is a researcher at the intersection of artificial intelligence and medical education, with a primary focus on optimizing deep learning for surgical skill assessment. His most cited work, "Towards Optimizing Convolutional Neural Networks for Robotic Surgery Skill Evaluation" (2019, 11 citations), addresses a critical challenge in surgical training: the subjectivity and bias inherent in traditional checklist-based evaluations. Pereira’s major contribution lies in demonstrating how convolutional neural networks can be fine-tuned to objectively analyze and score surgical performance from video data, offering a data-driven alternative to human assessment. By reducing inter-evaluator variability, his approach promises more consistent, scalable, and fair evaluations for surgeon residents. This work has implications for both robotic surgery training and broader competency-based medical education. Pereira’s research is notable for bridging computer vision with clinical pedagogy, providing a foundation for automated feedback systems that could enhance learning outcomes. With a growing citation footprint, he is establishing himself as a key voice in AI-assisted medical training, where his innovations continue to influence how institutions measure and improve surgical proficiency.
Research Focus
Key Achievements
Top Papers
- 1