Clinical Utility of Computer-Aided Diagnosis of Vertebral Fractures From Computed Tomography Images.

Osteoporotic vertebral compression fractures (VCFs) are a risk factor for morbidity and mortality, frequently asymptomatic and often present in computed tomography (CT) scans performed for unrelated conditions. Computer-aided diagnosis (CAD) of VCF from such images can potentially improve identification and treatment of osteoporosis. This single-blinded, single tertiary center study compared a CAD (Zebra Medical Vision®) to an adjudicated imaging specialist reevaluation using a retrospective consecutive sample of abdominal and thoracic CT scans (n = 2357) performed as part of routine care. Subjects over 50 years between January 1, 2019 and May 12, 2019 were included. Duplicates and unanalyzable scans were excluded resulting in a total of 1696 CT scans. The sensitivity, specificity, and accuracy were calculated for all VCF and for Genant grades 2 or 3 (ie, height loss of >25%) using imaging specialist as the gold standard. Prestudy VCF reporting by hospital-rostered radiologist was used to calculate the number of scans needed to screen (NNS) to detect one additional VCF using CAD. Prevalence of any VCF was 24% (406/1696) and of Genant 2/3 VCF was 18% (280/1570). The sensitivity and specificity were 54% and 92%, for all fractures, respectively, and 65% and 92% for Genant 2/3 fractures, respectively. Accuracy for any VCF, and for detection of Genant 2/3 VCF, was 83% and 88%, respectively. Of 221 CAD-detected VCFs, 133 (60.2%) were reported prestudy resulting in 88 additional fractures (72 Genant 2/3) being identified by CAD. NNS to detect one additional VCF was 19 scans for all fractures and 23 for Genant 2/3 fractures. Thus, the CAD tested in this study had a high specificity with moderate sensitivity to detect incidental vertebral fractures in CT scans performed for routine care. A low NNS suggests it is an efficient tool to assist radiologists and clinicians to improve detection and reporting of vertebral fractures.