Adolescent Idiopathic Scoliosis (AIS) is a deformation of the spine and it is routinely diagnosed using posteroanterior and lateral radiographs. The Risser sign used in skeletal maturity assessment is commonly accepted in AIS patient’s management. However, the Risser sign is subject to inter-observer variability and it relies mainly on the observation of ossification on the iliac crests. This study proposes a new machine-learning-based approach for Risser sign skeletal maturity assessment using EOS radiographs. Regions of interest including right and left humeral heads; left and right femoral heads; and pelvis are extracted from the radiographs. First, a total of 24 image features is extracted from EOS radiographs using a ResNet101-type convolutional neural network (CNN), pre-trained from the ImageNet database. Then, a support vector machine (SVM) algorithm is used for the final Risser sign classification. The experimental results demonstrate an overall accuracy of 84%, 78%, and 80% respectively for iliac crests, humeral heads, and femoral heads. Class activation maps using Grad-CAM were also investigated to understand the features of our model. In conclusion, our machine learning approach is promising to incorporate a large number of image features for different regions of interest to improve Risser grading for skeletal maturity. Automatic classification could contribute to the management of AIS patients.
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