Safaa Albasri
Papers
2
Total Citations
11
H-Index
2
About
Safaa Albasri is a researcher advancing the field of automated surgical training through computational motion analysis. Her primary research areas include surgical skill evaluation, task recognition, and the application of geometric and temporal alignment techniques to surgical robotics. In her highly cited 2019 paper, "A Novel Distance for Automated Surgical Skill Evaluation" (7 citations), Albasri introduced an innovative framework that uses sensor-captured surgical motion to objectively assess a surgeon’s proficiency—a critical step toward replacing subjective human evaluation with automated, data-driven training. Her follow-up work, "Surgery Task Classification Using Procrustes Analysis" (4 citations), further demonstrates her impact by combining Dynamic Time Warping with Procrustes analysis to accurately recognize surgical tasks from motion data. These contributions provide foundational methods for improving robotic surgery training, enabling more precise, consistent, and scalable skill assessment. Albasri’s work is notable for bridging geometric statistics and machine learning to solve real-world challenges in medical education, offering practical tools that could transform how surgeons are trained and evaluated.
Research Focus
Key Achievements
Top Papers
- 1A Novel Distance for Automated Surgical Skill Evaluation7 citations · 2019
- 2Surgery Task Classification Using Procrustes Analysis4 citations · 2019