Michael Ka-Shing Lee
Papers
1
Total Citations
28
H-Index
1
About
Michael Ka-Shing Lee is a pioneering researcher in robotic ultrasound imaging and intelligent medical navigation, whose work bridges surgical robotics with machine learning to transform scoliosis assessment. His most influential contribution, "Follow the Curve: Robotic Ultrasound Navigation With Learning-Based Localization of Spinous Processes for Scoliosis Assessment" (2022, 28 citations), introduces a novel framework that autonomously guides an ultrasound probe along the spine by learning to localize spinous processes from noisy images. This innovation addresses a critical clinical need—radiation-free monitoring of adolescent idiopathic scoliosis progression—by replacing repeated X-ray exposures with safe, automated ultrasound scans. Lee’s approach overcomes the inherent challenges of speckle noise in ultrasound through deep learning-based feature detection, enabling precise, real-time navigation that follows the spinal curvature. His work exemplifies a convergence of robotics, computer vision, and clinical orthopedics, offering a scalable solution for non-invasive, longitudinal scoliosis tracking. With growing citation impact, Lee is recognized for advancing point-of-care ultrasound robotics, and his research continues to shape the future of autonomous diagnostic imaging in musculoskeletal medicine.
Research Focus
Key Achievements
Top Papers
- 1