Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography

Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical disease, treating osteoporosis is an important strategy. This study aimed to validate the diagnostic yield of a deep learning algorithm for detecting osteopenia/osteoporosis using cervical radiography and compare its diagnostic accuracy with that of spine surgeons. Samples were divided into training (n = 200) and test (n = 30) datasets. The deep learning algorithm, designed to detect T-scores of the femoral neck or lumbar spine <-1.0 using cervical radiography, was constructed using a convolutional neural network model. The number of correct diagnoses was compared between the algorithm and nine spine surgeons using the independent test dataset. The results indicated that the algorithm's diagnostic accuracy, sensitivity, and specificity in the independent test dataset were 0.800, 0.818, and 0.750, respectively. The rate of corrected answers by the deep learning algorithm was significantly higher than that of nine spine surgeons in the test dataset (80.0% vs. 60.6%; p = 0.032). In conclusion, the diagnostic yield of the algorithm was higher than that of spine surgeons.