Prof. Mamrou Mitsuishi, School of Engineering, University of Tokyo, Japan

Title

Surgical system of the future based on production engineering and the integration of AI technology

Abstract

The author’s group is now developing a surgery assistant system based on production engineering principles. Anticipated future surgical systems can be categorized into three generations. In the first generation, x-rays, CT and MRI are used for medical imaging with a resolution of approximately 0.1 millimeter for surgeries at the organ level, with millimeter order precision. The operator is a qualified surgeon, even though a robotic system executes the surgery, and diagnosis and treatment are carried out by the surgeon. In the second generation, micrometer order 3D imaging of the body is available. Real-time, continuous imaging of the displacement and deformation of internal organs, such as the heart, is available. The scale of the surgery is cell level and micrometer order, where targeted therapy is realized. In this generation, the diagnosis and treatment are integrated. In the third generation, imaging is shifting from shape to functional analysis. The scale of the diagnosis and treatment is on the order of nanometers, enabling regenerative treatment. Both diagnosis and treatment are automated, with automatic diagnosis performed directly from the medical image.

In the presentation, I will talk about a technology to increase bone cutting accuracy as an example of the first generation surgical system. I will also discuss technology to reduce damage to the bone during cutting and to increase the adhesion force between bone and artificial joints. I will also introduce a transnasal pituitary surgery system, where the target is dura mater suturing, as one example of neurosurgery. The required technologies include a surgical robot at the bedside adapted from an industrial robot, a robot with sensors and miniature surgical instruments, an intuitive user interface, and acquisition of expert technique through machine learning. In the future, surgical systems with AI technology will spread rapidly. AI medical systems are different from conventional medical devices and systems in the following aspects: (1) AI medical systems are capable of self-adapting their performance through learning. They have qualitatively different plasticity from conventional devices and systems. (2) The output of the AI system may be unpredictable, due to the black box nature of the machine learning algorithms typically used in deep learning. (3) Further advances in AI medical systems may modify the relationship between doctors and patients. (4) Quality control of data is critical.
Biography
Prof. Mitsuishi is currently the executive director and vice president of the University of Tokyo, posts he has held since the start of the 2017 academic year. He was previously the Dean of the School of Engineering.He graduated from the Faculty of Science at the University of Tokyo in 1979 with a bachelor of science in physics. Following this, he earned a second bachelor’s degree in mechanical engineering from the Faculty of Engineering at the University of Tokyo in 1981. He continued his studies and obtained both his master’s degree and his PhD in mechanical engineering from the Graduate School of Engineering at the University of Tokyo (in 1983 and 1986, respectively). He became a professor in the department in 1999. Prof. Mitsuishi’s areas of interest are biomedical robotics (including computer-integrated surgical systems), and manufacturing systems (including the fields of multi-sensor integrated intelligent manufacturing systems and biomanufacturing). He is a member of various internationally renowned societies, such as the International Academy for Production Engineering (CIRP), where he is a vice president and fellow, and the IEEE Robotics and Automation Society.