Opportunistic <scp>CT</scp> Screening—Machine Learning Algorithm Identifies Majority of Vertebral Compression Fractures: A Cohort Study
John H. Page, Franklin G. Moser, M. Marcel Maya, Ravi S. Prasad, Barry D. Pressman
Abstract
ABSTRACT Vertebral compression fractures (VCF) are common in patients older than 50 years but are often undiagnosed. Zebra Medical Imaging developed a VCF detection algorithm, with machine learning, to detect VCFs from CT images of the chest and/or abdomen/pelvis. In this study, we evaluated the diagnostic performance of the algorithm in identifying VCF. We conducted a blinded validation study to estimate the operating characteristics of the algorithm in identifying VCFs using previously completed CT scans from 1200 women and men aged 50 years and older at a tertiary‐care center. Each scan was independently evaluated by two of three neuroradiologists to identify and grade VCF. Disagreements were resolved by a senior neuroradiologist. The algorithm evaluated the CT scans in a separate workstream. The VCF algorithm was not able to evaluate CT scans for 113 participants. Of the remaining 1087 study participants, 588 (54%) were women. Median age was 73 years (range 51–102 years; interquartile range 66–81). For the 1087 algorithm‐evaluated participants, the sensitivity and specificity of the VCF algorithm in diagnosing any VCF were 0.66 (95% confidence interval [CI] 0.59–0.72) and 0.90 (95% CI 0.88–0.92), respectively, and for diagnosing moderate/severe VCF were 0.78 (95% CI 0.70–0.85) and 0.87 (95% CI 0.85–0.89), respectively. Implementing this VCF algorithm within radiology systems may help to identify patients at increased fracture risk and could support the diagnosis of osteoporosis and facilitate appropriate therapy. © 2023 Amgen, Inc. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.