Challenge Grant: Image-based Detection for Rheumatoid Arthritis with Synthetic Data

• Return to Project List

Summary

This project aims to develop a machine learning model that detects inflammation caused by rheumatoid arthritis from hand images captured by a camera, with the goal of enabling early diagnosis and application in remote healthcare. To address the challenges of limited and imbalanced medical image data, synthetic data simulating the hands of rheumatoid arthritis patients are generated and used to improve prediction accuracy. By developing a diagnostic method that does not require specialists or expensive equipment our project also aims to help reduce healthcare disparities.

SDGs

Project Members

About Project Members, Researchers

Note: ◎ indicates the project leader

Name	Affiliation	Position	Field of Specialization/Research Interests
◎ Mariko Isogawa	Faculty of Science and Technology	Associate Professor	Computer Vision, Sensing, Deep Learning
Yasushi Kondo	School of Medicine	Senior Assistant Professor	Rheumatology, Clinical immunology, Cytokinology
Yoshimitsu Aoki	Faculty of Science and Technology	Professor	Image Processing, Computer Vision, Pattern Recognition and Artificial Intelligent System
Shun Kato	Graduate School of Science and Technology	Master Program	Image based machine learning
Yota Koshimoto	Graduate School of Science and Technology	Master Program	Image based region of interest extraction
Rintaro Chiba	Faculty of Science and Technology	Student	Image based machine learning
Daiki Kitsunai	Faculty of Science and Technology	Student	Synthetic data generation