リウマチ画像解析の学習データとAIベンチマークを公開～1,048手のX線データセットがリウマチ診断支援の進化を加速～

2026-03-30

2026-03-27 北海道大学,東京科学大学

北海道大学と東京科学大学の研究チームは、関節リウマチ診断支援に向けた手首X線画像データセット「RAM-W600」とAIベンチマークを公開した。388名・1,048枚の画像に対し、骨のセグメンテーションや標準評価法（SvdHスコア）による詳細なアノテーションを付与し、U-NetやTransUNetなど複数の深層学習モデルで性能比較を実施した。これにより、従来困難だった手首の複雑な骨構造の解析が可能となり、アルゴリズム開発や診断支援の高度化を促進する基盤が整備された。本データは他の骨疾患解析にも応用可能で、医療AI研究の発展に寄与する。

公開されたマルチタスクデータセットとAIベンチマーク

＜関連情報＞

RAM-W600：関節リウマチに向けた手首関節マルチタスクデータセットとベンチマーク RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis

Songxiao Yang, Haolin Wang, Yao Fu, Ye Tian, Tamotsu Kamishima, Masayuki Ikebe, Yafei Ou, Masatoshi Okutomi
arXiv last revised 6 Oct 2025 (this version, v3)
DOI:https://doi.org/10.48550/arXiv.2507.05193

Abstract

Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations. (i) The wrist comprises numerous small bones with narrow joint spaces, complex structures, and frequent overlaps, requiring detailed anatomical knowledge for accurate annotation. (ii) Disease progression in RA often leads to osteophyte, bone erosion (BE), and even bony ankylosis, which alter bone morphology and increase annotation difficulty, necessitating expertise in rheumatology. This work presents a multi-task dataset for wrist bone in CR, including two tasks: (i) wrist bone instance segmentation and (ii) Sharp/van der Heijde (SvdH) BE scoring, which is the first public resource for wrist bone instance segmentation. This dataset comprises 1048 wrist conventional radiographs of 388 patients from six medical centers, with pixel-level instance segmentation annotations for 618 images and SvdH BE scores for 800 images. This dataset can potentially support a wide range of research tasks related to RA, including joint space narrowing (JSN) progression quantification, BE detection, bone deformity evaluation, and osteophyte detection. It may also be applied to other wrist-related tasks, such as carpal bone fracture localization. We hope this dataset will significantly lower the barrier to research on wrist RA and accelerate progress in CAD research within the RA-related domain.