This thesis lays the groundwork for the automatic supervision of the laser incision process, which aims to complement surgeons' perception of the state of tissues and enhance their control over laser incisions. The research problem is formulated as the estimation of variables that are representative of the state of tissues during laser cutting. Prior research in this area leveraged numerical computation methods that bear a high computational cost and are not straightforward to use in a surgical setting. This book proposes a novel solution to this problem, using models inspired by the ability of experienced surgeons to perform precise and clean laser cutting. It shows that these new models, which were extracted from experimental data using statistical learning techniques, are straightforward to use in a surgical setup, allowing greater precision in laser-based surgical procedures.
- International Booker Prize
- 2025 Women's Prize for Nonfiction
- The Arthur C. Clarke Award
- Uplifting Reads to Kickstart Your Year
- Bestsellers of 2024
- Nero Book Awards
- Great Reads from Around the World
- THE POLARI PRIZE
- World Poetry Day
- International Women's Day
- Business Book of the Year 2024
- Nobel Prize in Literature 2024 - Han Kang
- Reading Marathon September
- See all
- World Cancer Day
- International Day of Women and Girls in Science
- Magazines
- World Photography Day
- See all