DeepRA: Predicting Joint Damage From Radiographs Using CNN with Attention
This work addresses a tedious and subjective task for medical experts in rheumatology, offering an incremental improvement in automated assessment.
The paper tackles the problem of manually assessing joint damage in Rheumatoid Arthritis from radiographs by proposing a two-staged CNN with attention method, achieving a 31% improvement in predicting joint narrowing scores and a 79% improvement in overall damage prediction compared to baseline.
Joint damage in Rheumatoid Arthritis (RA) is assessed by manually inspecting and grading radiographs of hands and feet. This is a tedious task which requires trained experts whose subjective assessment leads to low inter-rater agreement. An algorithm which can automatically predict the joint level damage in hands and feet can help optimize this process, which will eventually aid the doctors in better patient care and research. In this paper, we propose a two-staged approach which amalgamates object detection and convolution neural networks with attention which can efficiently and accurately predict the overall and joint level narrowing and erosion from patients radiographs. This approach has been evaluated on hands and feet radiographs of patients suffering from RA and has achieved a weighted root mean squared error (RMSE) of 1.358 and 1.404 in predicting joint level narrowing and erosion Sharp van der Heijde (SvH) scores which is 31% and 19% improvement with respect to the baseline SvH scores, respectively. The proposed approach achieved a weighted absolute error of 1.456 in predicting the overall damage in hands and feet radiographs for the patients which is a 79% improvement as compared to the baseline. Our method also provides an inherent capability to provide explanations for model predictions using attention weights, which is essential given the black box nature of deep learning models. The proposed approach was developed during the RA2 Dream Challenge hosted by Dream Challenges and secured 4th and 8th position in predicting overall and joint level narrowing and erosion SvH scores from radiographs.