Anjali Jaiprakash
Queensland University of Technology, Australian Centre for Robotic Vision
Papers
16
Total Citations
316
H-Index
10
About
Anjali Jaiprakash is a pioneering researcher at the intersection of medical robotics, ultrasound imaging, and minimally invasive surgery, with a particular focus on transforming orthopaedic procedures through intelligent automation. Her work has significantly advanced the field of robotic knee arthroscopy, combining deep learning, real-time ultrasound guidance, and robotic systems to improve surgical precision and patient outcomes. Among her most influential contributions is her research on ultrasound-guided robotic procedures, which has garnered 69 citations, alongside landmark work applying deep learning to femoral cartilage segmentation in ultrasound imaging — a technically demanding challenge given the modality's inherent complexity. Her Bayesian CNN approaches for uncertainty inference in 4D ultrasound imaging further demonstrate her commitment to robust, clinically reliable AI solutions. Notably, Jaiprakash has also bridged the gap between technology and clinical practice by surveying orthopaedic surgeons' attitudes toward robotic adoption, ensuring her engineering innovations remain grounded in real surgical needs. With over 280 cumulative citations across her top works, her research portfolio reflects both technical depth and practical vision. From designing micro end effectors for concentric tube robots to developing volumetric knee atlases, Jaiprakash's contributions are shaping the future of autonomous, image-guided surgical robotics.
Research Focus
Key Achievements
Top Papers
- 1Ultrasound guidance in minimally invasive robotic procedures69 citations · 2019
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- 4Robotic and Image-Guided Knee Arthroscopy27 citations · 2019
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- 6Kinematic Model of the Human Leg Using DH Parameters17 citations · 2020
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