[Yoshito Otake] Closed-loop Computer Integrated Surgery using Statistical Learning and Biological Function Simulation

Presto Researcher

Yoshito Otake
Graduate School of Information Science, Nara Institute of Science and Technology
Associate Professor

Outline

To make an appropriate decision in surgery, surgeons estimate the location and shape of an invisible target organ based on their knowledge, experience and limited measurements (e.g. visual observation, 2D x-ray projection, electrocardiogram, and tactile sense). This project proposes a system allowing quantitative and accurate estimation, hence a safer surgery, by efficiently utilizing statistical learning of a large-scale surgical record database (referred to as “medical big data”). The improvement in surgical safety produced by the learning of medical records is expected to favorably impact on a controversial issue of the balance between patient’s benefit and privacy concerns. As a result, the system is expected to raise the social acceptability of medical big data, which encourages further collection of the training dataset, which ends up improving the system’s performance. This closed-loop data collection and utilization framework will be experimentally demonstrated in the project.

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