Matthew Holden
Papers
2
Total Citations
9
H-Index
2
About
Matthew Holden is a pioneering researcher at the intersection of surgical data science, human-robot interaction, and medical artificial intelligence. His work focuses on developing objective, automated methods for evaluating surgical performance, with a particular emphasis on robot-assisted surgery (RAS) and ophthalmology. In a landmark 2024 study, Holden introduced a novel machine learning framework that integrates electroencephalogram (EEG) and eye-tracking data to classify subtask types and skill levels in RAS, achieving 7 citations and establishing a new paradigm for real-time, neurocognitive assessment of surgical expertise. He further advanced the field by developing an ensemble of 2D–3D convolutional neural networks for performance evaluation in cataract surgery, addressing the long-standing challenge of subjective, time-consuming expert review. This work, with 2 citations, demonstrates how deep learning can automate skill assessment from surgical video, reducing bias and labor. Holden’s contributions are foundational to the emerging field of surgical AI, promising to enhance training efficiency and patient safety. His research has been recognized for its interdisciplinary impact, bridging cognitive neuroscience, computer vision, and clinical practice.
Research Focus
Key Achievements
Top Papers
- 1
- 2