Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography

DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use.

Osteoporosis is defined as a systemic disease of the bone characterized by a decrease in bone strength and deterioration of bone structure at the microscopic level, leading to bone fragility and increased risk of fracture. Bone mineral density (BMD) is the preferred method for the diagnosis of osteoporosis, and dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Conventional radiography is more suited for the screening of osteoporosis rather than diagnosis, and osteoporosis can be detected on radiographs by experienced physicians only. This study explored the possibility of predicting BMD relative to DXA using patient radiographs.

A deep learning algorithm of convolutional neural network (CNN) was used for the purpose. The method includes image segmentation, CNN learning, and a convolution-based regression model (DeepDXA) that links the isolated images of the femur bone to predict BMD value. Data were obtained in a single medical center from 2006 to 2018, with a total amount of 3472 pairs of pelvis X-ray and DXA examination within 1 year.

The proposed workflow successfully predicted BMD values of the femur bone with the correlation coefficient (R) of 0.85 (P < 0.001) and the accuracy of 0.88 for prediction osteoporosis, a finding that could be reliably ready for further clinical use.

When suspicious osteoporosis is seen on plain films using the deep learning method we developed, further referral to DXA for the definite diagnosis of osteoporosis is indicated.