Keerthi Ram

Papers

Total Citations

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About

Keerthi Ram is a leading researcher in biomedical image analysis and surgical robotics, with a focus on advancing computer-assisted interventions. His work bridges the gap between deep learning and clinical practice, particularly in orthopedics and spine surgery. Ram’s most cited paper, “Segmentation of Femoral Cartilage from Knee Ultrasound Images Using Mask R-CNN” (2019, 36 citations), introduced a pioneering application of instance segmentation for osteoarthritis diagnosis and robotic knee arthroscopy guidance. This contribution demonstrated how deep learning can extract clinically relevant structures from challenging ultrasound data, enabling non-invasive assessment of cartilage health. More recently, his 2024 work on “A Hybrid-Layered System for Image-Guided Navigation and Robot-Assisted Spine Surgeries” (3 citations) presents a comprehensive, cost-effective system integrating navigation and robotic assistance for precise spinal interventions. This system addresses the growing demand for accessible surgical technologies by combining cutting-edge hardware and software into a unified platform. Ram’s research consistently emphasizes translational impact, developing solutions that enhance surgical accuracy while reducing costs. His contributions are shaping the future of image-guided therapy, making complex procedures safer and more reproducible for patients worldwide.