Samar M. Alsaleh
Papers
9
Total Citations
239
H-Index
7
About
Samar M. Alsaleh is a leading researcher in robotic-assisted surgery, specializing in restoring the critical sense of touch that is lost in minimally invasive procedures. Her work centers on sensorless force estimation, sensory substitution, and the application of deep learning and neuro-fuzzy systems to surgical robotics. Her most impactful contribution is the development of a supervised neuro-recurrent-vision approach for retrieving force feedback, a paper that has garnered over 100 citations and addresses a major limitation in robotic surgery: the inability for surgeons to feel the forces they apply to tissue. Alsaleh has pioneered methods that use visual data—such as 3D deformation recovery and specularity detection—to estimate interaction forces without physical sensors, making her solutions practical for clinical settings. Her work on deep-neuro-fuzzy systems and dimensionality reduction in deep networks further advances the accuracy and robustness of force estimation under visual uncertainty. With over 200 total citations across her publications, Alsaleh’s research is pivotal in enhancing surgical precision, reducing tissue damage, and improving patient outcomes in robotic-assisted surgeries.
Research Focus
Key Achievements
Top Papers
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- 5Sensory Substitution for Force Feedback Recovery16 citations · 2018
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- 9Towards robust specularity detection and inpainting in cardiac images2 citations · 2016