Development of a preoperative accuracy prediction model using machine learning for implant placement in static-guided surgery: retrospective observational study
Takuya Mino, Yurina Matsuoka, Ken’ichi Morooka, Kana Tokumoto, Hiroaki Shimizu, Yoko Kurosaki, Aya Kimura-Ono, Hiromitsu Kishimoto, Takuo Kuboki, Kenji Maekawa · 2026
Read Development of a preoperative accuracy prediction model using machine learning for implant placement in static-guided surgery: retrospective observational study by Takuya Mino, Yurina Matsuoka, Ken’ichi Morooka, Kana Tokumoto, Hiroaki Shimizu, Yoko Kurosaki, Aya Kimura-Ono, Hiromitsu Kishimoto, Takuo Kuboki, Kenji Maekawa (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/PMC13538333.txt.
Description
Takuya Mino and colleagues build a learning model to predict dental implant placement error before guided surgery in a 2026 study. Guided surgery follows a digital plan, yet placed implants still drift from planned spots, risking nearby nerves and restorations. Data held 181 patients and 480 implants placed with full static guides. Error at the implant tip was split at a half-millimeter line, with twenty-one clinical and scan factors as predictors.
Twelve factors linked to placement error in importance checks. A stacked combined model beat single models, correctly spotting most larger errors and most smaller errors under repeated cross-checks. The authors suggest the model can support pre-surgery review of expected accuracy, helping clinicians adjust plans before operating rather than discovering drift afterward.
Catalog record
| CREATOR | Takuya Mino, Yurina Matsuoka, Ken’ichi Morooka, Kana Tokumoto, Hiroaki Shimizu, Yoko Kurosaki, Aya Kimura-Ono, Hiromitsu Kishimoto, Takuo Kuboki, Kenji Maekawa |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35450 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |