Fundamental kinematic indicators predict surgical expertise and clinical outcomes in robot-assisted surgery
Hideto Ueki, Takuto Hara, Tomoko Yamaguchi, Naoto Wakita, Yasuyoshi Okamura, Kotaro Suzuki, Yukari Bando, Kiyoyuki Chinzei, Munenori Uemura, Tomoaki Terakawa, Akihisa Yao, Koji Chiba · 2026
Read Fundamental kinematic indicators predict surgical expertise and clinical outcomes in robot-assisted surgery by Hideto Ueki, Takuto Hara, Tomoko Yamaguchi, Naoto Wakita, Yasuyoshi Okamura, Kotaro Suzuki, Yukari Bando, Kiyoyuki Chinzei, Munenori Uemura, Tomoaki Terakawa, Akihisa Yao, Koji Chiba (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13623960.txt.
Description
Hideto Ueki and colleagues test whether the bare basics of hand movement, captured as three-dimensional positions, can reveal a surgeon's skill on a robotic operating system. Robotic platforms each hoard their own detailed data, which blocks shared standards for judging performance. If plain position data were enough, any robot with tracking could be scored the same way.
Working on Japan's Hinotori robot, the team fed movement features into a machine learning classifier that separated experts from novices with high accuracy. One feature led the pack: how densely the left hand's movements clustered at their peak. A small set of measures still worked when whole surgeons were held out of training, which suggests the signal generalises. Movement patterns also tracked patient outcomes, including blood loss, and experts adapting to the new robot followed visibly different learning paths. The authors propose tailored training based on each surgeon's movement fingerprint.
Catalog record
| CREATOR | Hideto Ueki, Takuto Hara, Tomoko Yamaguchi, Naoto Wakita, Yasuyoshi Okamura, Kotaro Suzuki, Yukari Bando, Kiyoyuki Chinzei, Munenori Uemura, Tomoaki Terakawa, Akihisa Yao, Koji Chiba |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |