Fine-Grained Differentiation-Based GNN for Auxiliary Diagnosis of Kashin-Beck Disease.
Authors
Affiliations (5)
Affiliations (5)
- Arthritis Clinic and Research Center, Peking University People's Hospital, No. 11 Xicheng District, Beijing, China.
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing, China.
- BUPT Hainan Advanced Digital Technology and Systems Laboratory, Hainan Lingshui Li'an International Education Innovation Experimental Zone, Hainan, China.
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing, China. [email protected].
- BUPT Hainan Advanced Digital Technology and Systems Laboratory, Hainan Lingshui Li'an International Education Innovation Experimental Zone, Hainan, China. [email protected].
Abstract
Kashin-Beck Disease (KBD), a localized osteoarthropathy, can lead to severe dwarfism and deformities, making early intervention crucial. Manual radiographic assessment of KBD can be challenging due to subtle changes of lesion features and overlapping features with other osteoarthropathies. Incorporating the radiological features of the foot is vital for diagnosing atypical KBD cases with unclear hand lesions. This paper employs graph neural networks (GNNs) to enhance feature differentiation in susceptible areas, distinguishing KBD lesions from similar conditions and aiding diagnosis. We identified seven regions in hand and ankle X-ray images based on prior knowledge as primary diagnostic focuses. Superpixel segmentation is applied within these regions, considering feature similarity and spatial proximity. A graph network is established with pixel blocks as nodes, highlighting lesion differences through edge weight variations. Finally, a convolutional neural network (CNN) extracts global features, enhancing focus on susceptible regions for precise KBD diagnosis. Experimental results show high diagnostic accuracy (90.97%) and recall (97.93%), with an overall F1-score of 93.97%, supporting the feasibility of large-scale automatic KBD diagnosis.