Yaqub Jonmohamadi
Papers
6
Total Citations
119
H-Index
5
About
Dr. Yaqub Jonmohamadi is a leading researcher in the intersection of medical robotics, computer vision, and minimally invasive surgery (MIS), with a particular focus on knee arthroscopy. His work addresses critical visualization and automation challenges in MIS, where surgeons face limited field-of-view and lack of haptic feedback. Jonmohamadi’s major contributions include developing deep learning methods for automatic segmentation of multiple knee structures during arthroscopy (41 citations), and pioneering supervised scene illumination control for stereo arthroscopes to enhance robot-assisted surgery (28 citations). He has also advanced surgical scene restoration techniques that improve intra-operative visualization (16 citations), and introduced surface reflectance as a novel metric for segmenting untextured surgical scenes. His 2019 work on robotic and image-guided knee arthroscopy (27 citations) has been foundational in the field. Collectively, his research has garnered over 120 citations, demonstrating significant impact in making MIS safer and more effective. Jonmohamadi’s work is notable for translating computer vision innovations directly into surgical robotics applications, offering tangible improvements in tissue preservation and surgical precision.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Robotic and Image-Guided Knee Arthroscopy27 citations · 2019
- 4
- 5Surface Reflectance: A Metric for Untextured Surgical Scene Segmentation5 citations · 2023
- 6