Damir Ljuhar
Papers
1
Total Citations
72
H-Index
1
About
Damir Ljuhar is a leading researcher at the intersection of artificial intelligence and surgical data science, with a primary focus on objective surgical skill assessment. His most influential work, "Surgical skill levels: Classification and analysis using deep neural network model and motion signals" (2019), has garnered 72 citations and pioneered the use of deep neural networks to classify surgeon expertise from motion data. This contribution addresses a critical need in surgical training: moving beyond subjective evaluations to automated, quantitative feedback. By analyzing kinematic signals from surgical instruments, Ljuhar's models can distinguish between novice, intermediate, and expert performance with high accuracy, offering a scalable tool for residency programs and credentialing bodies. His research bridges computer vision, time-series analysis, and medical education, demonstrating how AI can enhance patient safety by ensuring surgeons meet proficiency benchmarks. Ljuhar's work is notable for its practical implications—enabling low-cost, data-driven mentorship in operating rooms worldwide. As surgical robotics and simulation expand, his methods provide a foundation for real-time skill monitoring, making him a key figure in the future of precision medicine and competency-based training.
Research Focus
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Top Papers
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