Using AI to better detect vertebral fractures
Small vertebral fractures, which can be common in diseases like osteoporosis, are often missed by traditional imaging techniques. New research, published in Radiology, suggests a deep learning algorithm can assist with detection and diagnosis, helping clinicians identify these small breaks before they become a more significant medical issue.
Researchers from the University of Manitoba developed an AI algorithm called a convolutional neural network (CNN) to help identify these small fractures. The group trained and tested the CNN on more than 12,000 dual x-ray absorptiometry images to assess whether a fracture was present. They learned that the CNN was able to correctly detect vertebral fractures under the receiving operating characteristic curve of 0.94, with a sensitivity of 87.4% and a specificity of 88.4%. When compared to expert readers, with at least 10 years of experience in reading such images, the CNN had an agreement rate of 0.76, leading the researchers to concluded that such algorithms could augment radiologist’s abilities to detect vertebral fractures, helping to increase accuracy of diagnosis and improve patient outcomes in the future.