Sapporo, Japan
Sapporo, Japan
Nara Institute of Science and Technology
(NAIST), Japan
Nara Medical University, Japan
Yoshinobu Sato received his B.S., M.S. and Ph.D. degrees in Information and Computer Sciences from Osaka University, Japan in 1982, 1984, and 1988, respectively. From 1988 to 1992, he worked at the NTT Human Interface Laboratories. In 1992, he joined Osaka University Medical School. From 1996 to 1997, he was a Visiting Research Fellow at the Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital. In 1999, he became an Associate Professor at Osaka University Graduate School of Medicine. From 2014 to 2025, he was a Professor at the Nara Institute of Science and Technology (NAIST). Currently, he is a Professor Emeritus of NAIST and an Invited Professor at Nara Medical University.
His research interests include medical image analysis, computer-assisted surgery, and computational anatomy. Dr. Sato is a recipient of the CAOS-International Maurice E. Müller Award for Excellence in Computer Assisted Surgery. He serves as an Editorial Board member of the Medical Image Analysis journal and IEEE Transactions on Medical Imaging, and is a Fellow of the MICCAI (Medical Image Computing and Computer Assisted Intervention) Society.
Speech Title: "Next-Generation Computer-Assisted Orthopedic Surgery: AI-Driven Musculoskeletal Image Analysis and Surgical Planning"
Abstract: Recent advancements in deep learning and image analysis technologies have enabled high-precision, automated recognition of musculoskeletal structures from medical images. In CT imaging, this allows for the automated, quantitative analysis of individual muscle shape, volume, and density alongside traditional bone metrics. By automatically processing large-scale clinical CT datasets, surgical information, and patient records, this approach provides objective, quantitative support for orthopedic surgical decision-making and planning—domains that have traditionally relied on empirical evaluation.
Based on these automated recognition results, we have developed large-scale training datasets to analyze age-related anatomical variations, track disease progression, and automate surgical planning. Furthermore, we are developing AI models to estimate quantitative musculoskeletal parameters directly from plain X-ray images. This framework facilitates complementary preoperative planning and surgical indication assessment using both CT and X-ray imaging, while also enabling low-dose postoperative evaluation and long-term preventive monitoring. In this keynote lecture, we present our musculoskeletal AI analysis infrastructure and discuss its practical applications for next-generation orthopedic surgery support.
Associate Professor Dr Mongkol Kaewbumrung is a researcher and educator in the Department of Mechanical Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi, Thailand. He holds a doctorate in applied mathematics from Mahidol University and a master’s degree in aerospace engineering from Kasetsart University.
His expertise encompasses computational fluid dynamics, turbulence modelling, multiphase flow, and finite element analysis. His biomedical research investigates non-Newtonian blood flow, particle transport, and wall shear stress in stenosed coronary arteries. In heritage science, he studies ventilation and moisture transport in historic Thai temples to understand environmental conditions associated with mural deterioration and support preventive conservation.
A distinctive focus of his work is using engineering simulation to support cultural heritage conservation. He studies airflow and humidity transport in historic Thai temples to investigate environmental conditions associated with mural deterioration and inform preventive conservation strategies. His research has been featured in an Ansys case study. In 2023, he served as a visiting lecturer with the CFDResearch group at RMIT University, Australia. Across these applications, his work connects applied mathematics with practical problems in engineering, healthcare, and heritage preservation.
Speech Title: "From Human Health to Heritage Health through a Transdisciplinary Computational Approach"
Abstract: The Navier–Stokes equations provide a mathematical bridge between blood flow in human arteries and airflow within historic buildings. This presentation explores how this shared foundation connects digital medicine, image processing, and heritage science through a transdisciplinary computational approach.
Drawing on case studies of stenosed coronary arteries and historic Thai temples, the presentation examines how geometry, material properties, and boundary conditions shape fluid motion across different environments. In biomedical applications, computational models investigate pressure distributions, particle transport, and wall shear stress. In heritage applications, airflow simulations, coupled with heat and moisture transport equations, help investigate environmental conditions associated with mural deterioration and support preventive conservation.
Image-derived geometries and spatial observations link physical systems to computational models. A transferable workflow integrates data acquisition, geometric reconstruction, numerical discretisation, simulation, verification, validation, and uncertainty assessment. Particular attention is given to adapting constitutive laws, transport models, and interpretation to the requirements of each discipline.
By placing the Navier–Stokes equations at the centre of this connection, the presentation demonstrates how a common mathematical language can generate research opportunities across healthcare and cultural heritage conservation.
