Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study

Abstract

Objectives: Vertebral fracture is both common and serious among adults, yet it often goes undiagnosed. This study aimed to develop a shape-based algorithm (SBA) for the automatic identification of vertebral fractures. Methods: The study included 144 participants (50 individuals with a fracture and 94 without a fracture) whose plain thoracolumbar spine X-rays were taken. Clinical diagnosis of vertebral fracture (grade 0 to 3) was made by rheumatologists using Genant's semiquantitative method. The SBA algorithm was developed to determine the ratio of vertebral body height loss. Based on the ratio, SBA classifies a vertebra into 4 classes: 0 = normal, 1 = mild fracture, 2 = moderate fracture, 3 = severe fracture). The concordance between clinical diagnosis and SBA-based classification was assessed at both person and vertebra levels. Results: At the person level, the SBA achieved a sensitivity of 100% and specificity of 62% (95% CI, 51%–72%). At the vertebra level, the SBA achieved a sensitivity of 84% (95% CI, 72%–93%), and a specificity of 88% (95% CI, 85%–90%). On average, the SBA took 0.3 s to assess each X-ray. Conclusions: The SBA developed here is a fast and efficient tool that can be used to systematically screen for asymptomatic vertebral fractures and reduce the workload of healthcare professionals.

Keywords

• Artificial intelligence
• X-ray
• Vertebra segmentation
• Vertebral fracture
• Shape-based algorithm