Kyungmin Jo
Papers
4
Total Citations
182
H-Index
3
About
Kyungmin Jo is a leading researcher in the field of surgical data science and medical robotics, with a primary focus on enhancing the safety and precision of robot-assisted laparoscopic surgery. Jo’s most significant contributions lie in the real-time detection and tracking of surgical instruments using deep learning. Their pioneering work, "Surgical-tools detection based on Convolutional Neural Network in laparoscopic robot-assisted surgery," has garnered 108 citations, establishing a foundational approach for automated tool recognition. Building on this, Jo developed a robust, real-time detection system that integrates Convolutional Neural Networks with motion vector prediction (63 citations), a critical advancement for providing intraoperative feedback and preventing post-operative complications. Further expanding their scope, Jo has also explored automatic hemorrhage detection in surgical images. Beyond surgical vision, their work extends to evaluating the safety and performance of wearable artificial kidney systems, demonstrating a broader interest in medical device safety. Through these contributions, Kyungmin Jo is helping to build the intelligent, data-driven operating room of the future.
Research Focus
Key Achievements
Top Papers
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