  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.
