A. Jonathan McLeod
Intuitive Surgical (United States), Western University, Robarts Clinical Trials
Papers
6
Total Citations
75
H-Index
4
About
A. Jonathan McLeod is a researcher at the intersection of computer vision, deep learning, and robotic surgery, whose work aims to enhance the perception and automation of minimally invasive procedures. His primary research areas include temporal segmentation of surgical tasks, 3D scene reconstruction from endoscopic video, and motion magnification for improved intraoperative visualization. McLeod’s most significant contribution is his pioneering use of deep learning with multiple data sources to temporally segment surgical sub-tasks in robot-assisted surgeries (RAS), a critical step toward automating complex procedures like suturing and tissue manipulation. This work, published in 2020, has garnered 38 citations and lays the foundation for finite-state machine representations of surgical workflows. He has also advanced stereoscopic scene reconstruction for robotic prostatectomy, enabling robust 3D visualization from uncalibrated stereo endoscopes, and developed motion magnification techniques to highlight subtle tissue movements—such as pulsatile blood flow—during endoscopic surgery, aiding vessel-sparing operations. With a total of over 75 citations across his publications, McLeod’s research is shaping the future of intelligent surgical systems, offering practical solutions to enhance surgeon feedback and pave the way for autonomous robotic assistance in the operating room.
Research Focus
Key Achievements
Top Papers
- 1
- 2Motion magnification for endoscopic surgery19 citations · 2014
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