Maria Paola Forte received her BSc degree in Biomedical Engineering from the University of Genova in 2015 with the thesis "Optical flow’s evaluation and segmentation for the analysis of crowds’ movements".
Forte earned her MSc degree in Bioengineering (Technologies for Electronics) at Politecnico di Milano in 2018. During her master, she participated in an exchange program at the University of Queensland (Brisbane).
Her graduate research "Robust Visual Augmented Reality in Robot-Assisted Surgery" was conducted at the Max Planck Institute for Intelligent Systems under the supervision of Dr. Katherine J. Kuchenbecker.
Forte's interdisciplinary background involves computer science, electronics, automation, and human physiology. Her main interests are robotic surgery, bio-inspired robotics and computer vision.
Politecnico di Milano, Milan, Italy, July 2018, Department of Electronic, Information, and Biomedical Engineering (mastersthesis)
The broader research objective of this line of research is to test the hypothesis that real-time stereo video analysis and augmented reality can increase safety and task efficiency in robot-assisted surgery.
This master’s thesis aims to solve the first step needed to achieve this goal: the creation of a robust system that delivers the envisioned feedback to a surgeon while he or she controls a surgical robot that is identical to those used on human patients.
Several approaches for applying augmented reality to da Vinci Surgical Systems have been proposed, but none of them entirely rely on a clinical robot; specifically, they require additional sensors, depend on access to the da Vinci API, are designed for a very specific task, or were tested on systems that are starkly different from those in clinical use. There has also been prior work that presents the real-world camera view and the computer graphics on separate screens, or not in real time. In other scenarios, the digital information is overlaid manually by the surgeons themselves or by computer scientists, rather than being generated automatically in response to the surgeon’s actions.
We attempted to overcome the aforementioned constraints by acquiring input signals from the da Vinci stereo endoscope and providing augmented reality to the console in real time (less than 150 ms delay, including the 62 ms of inherent latency of the da Vinci). The potential benefits of the resulting system are broad because it was built to be general, rather than customized for any specific task. The entire platform is compatible with any generation of the da Vinci System and does not require a dVRK (da Vinci Research Kit) or access to the API. Thus, it can be applied to existing da Vinci Systems in operating rooms around the world.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems