Vertebral compression fractures at abdominal CT: underdiagnosis, undertreatment, and evaluation of an AI algorithm

Abstract

Abstract Vertebral compression fractures (VCFs) are common and indicate a high future risk of additional osteoporotic fractures. However, many VCFs are unreported by radiologists, and even if reported, many patients do not receive treatment. The purpose of the study was to evaluate a new artificial intelligence (AI) algorithm for the detection of VCFs and to assess the prevalence of reported and unreported VCFs. This retrospective cohort study included patients over age 60 yr with an abdominal CT between January 18, 2019 and January 18, 2020. Images and radiology reports were reviewed to identify reported and unreported VCFs, and the images were processed by an AI algorithm. For reported VCFs, the electronic health records were reviewed regarding subsequent osteoporosis screening and treatment. Totally, 1112 patients were included. Of these, 187 patients (16.8%) had a VCF, of which 62 had an incident VCF and 49 had a previously unknown prevalent VCF. The radiologist reporting rate of these VCFs was 30% (33/111). For moderate and severe (grade 2–3) VCF, the AI algorithm had 85.2% sensitivity, 92.3% specificity, 57.8% positive predictive value, and 98.1% negative predictive value. Three of 30 patients with reported VCFs started osteoporosis treatment within a year. The AI algorithm had high accuracy for the detection of VCFs and could be very useful in increasing the detection rate of VCFs, as there was a substantial underdiagnosis of VCFs. However, as undertreatment in reported cases was substantial, to fully realize the potential of AI, changes to the management pathway outside of the radiology department are imperative.