3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT
This addresses the clinical need for automated fracture detection in osteoporosis management, though it appears incremental as it builds on existing CNN and sequence-to-sequence methods.
The study tackled the problem of early detection of vertebral compression fractures in CT images to prevent major osteoporosis-related fractures, achieving a patient-level fracture identification AUC of 0.955.
An osteoporosis-related fracture occurs every three seconds worldwide, affecting one in three women and one in five men aged over 50. The early detection of at-risk patients facilitates effective and well-evidenced preventative interventions, reducing the incidence of major osteoporotic fractures. In this study, we present an automatic system for identification of vertebral compression fractures on Computed Tomography images, which are often an undiagnosed precursor to major osteoporosis-related fractures. The system integrates a compact 3D representation of the spine, utilizing a Convolutional Neural Network (CNN) for spinal cord detection and a novel end-to-end sequence to sequence 3D architecture. We evaluate several model variants that exploit different representation and classification approaches and present a framework combining an ensemble of models that achieves state of the art results, validated on a large data set, with a patient-level fracture identification of 0.955 Area Under the Curve (AUC). The system proposed has the potential to support osteoporosis clinical management, improve treatment pathways, and to change the course of one of the most burdensome diseases of our generation.