This work introduces a comprehensive dataset designed to advance AI-driven surgical robotics and medical imaging. By capturing detailed ๐ฎ๐ป๐ฎ๐๐ผ๐บ๐ถ๐ฐ๐ฎ๐น ๐น๐ฎ๐ป๐ฑ๐บ๐ฎ๐ฟ๐ธ๐ ๐ผ๐ณ ๐๐ต๐ฒ ๐๐ฝ๐ฝ๐ฒ๐ฟ ๐ฎ๐ถ๐ฟ๐๐ฎ๐, we aim to support safer, more accurate ๐ฏ๐ฟ๐ผ๐ป๐ฐ๐ต๐ผ๐๐ฐ๐ผ๐ฝ๐ ๐ฎ๐ป๐ฑ ๐ถ๐ป๐๐๐ฏ๐ฎ๐๐ถ๐ผ๐ป procedures โ paving the way for improved patient outcomes and robust benchmarking in clinical AI.
๐ ๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐:
– First-of-its-kind dataset focused on airway anatomical landmarks
– Enables benchmarking for automated navigation and intubation tasks
– Openly available to foster collaboration across robotics, AI, and healthcare communities
We hope this resource will accelerate innovation in ๐ฒ๐บ๐ฏ๐ผ๐ฑ๐ถ๐ฒ๐ฑ ๐ถ๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐ต๐ฒ๐ฎ๐น๐๐ต๐ฐ๐ฎ๐ฟ๐ฒ and inspire new interdisciplinary collaborations.
๐ Read the full paper: https://rdcu.be/eS0d5
Grateful to all co-authors and collaborators from The Chinese University of Hong Kong (Ruoyi Hao, Zhiqing Tang, Catherine Po Ling Chan, Jason Ying Kuen Chan, Prof. Hongliang Ren), Hubei University of Technology (Zhang Yang), Huazhong University of Science and Technology (Yang Zhou), National University of Singapore (Lalithkumar Seenivasan), and Singapore General Hospital (Shuhui
Xu, Neville Wei Yang Teo, Kaijun Tay, Vanessa Yee Jueen Tan, Jiun Fong Thong, Kimberley Liqin Kiong, Shaun Loh, Song Tar Toh, and Prof. Chwee Ming Lim), for making this possible. Excited to see how others build upon this foundation!
