Tian‐Ci Yang

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About

Tian-Ci Yang is a leading researcher in spinal surgery, specializing in the integration of artificial intelligence and robotic technologies to enhance surgical precision and patient outcomes. Their primary research areas include automated pedicle screw planning, robot-assisted spine surgery, and minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF). Yang's major contributions include the development and validation of a three-dimensional U-Net algorithm for automated pedicle screw planning, demonstrating feasibility in thoracolumbosacral regions through training on over 1,200 cases. This work, published in 2025, has already garnered 3 citations, highlighting its early impact. Additionally, Yang has investigated clinical outcomes comparing robot-assisted versus conventional free-hand techniques in spine surgery, as well as risk factors for pedicle screw placement deviation in robot-assisted MIS-TLIF. These studies, each with 1 citation, provide critical insights into optimizing surgical safety and efficacy. Yang's research is notable for bridging cutting-edge AI with practical surgical applications, offering a pathway toward more accurate, less invasive spinal procedures. Their work is essential reading for clinicians and researchers seeking to understand the future of technology-driven spine surgery.