Abstract
Vertebral compression fractures (VCF) 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 years with an abdominal CT between 18 January 2019 and 18 January 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 medical 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% PPV and 98.1% NPV. 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.
Lay Summary
Vertebral compression fractures (VCF) are the most common osteoporotic fractures. However, they often go undetected leading to a high risk of further fractures. In this study we tested a new artificial intelligence (AI) algorithm to detect VCFs in abdominal CT scans in patients over 60 years of age, and assessed how often VCFs were missed by radiologists. We found that VCFs were underreported, with only 30% being identified by the radiologists. The AI algorithm showed promising results and had high accuracy for detecting VCFs. However, many patients with a detected VCF still did not receive treatment. The results suggests that AI could increase the detection rate of VCFs, but also highlights the need for changes beyond radiology to ensure that patients with detected fractures are appropriately treated.
Accepted manuscripts
