Automated vertebral compression fracture detection and quantification on opportunistic CT scans: a performance evaluation

Abstract

AIM

Since the majority of vertebral compression fractures (VCFs) are asymptomatic, they often go undetected on opportunistic CT scans. To reduce rates of undiagnosed osteoporosis, we developed a deep learning (DL)-based algorithm using 2D/3D U-Nets convolutional neural networks to opportunistically screen for VCF on CT scans. This study aimed to evaluate the performance of the algorithm using external real-world data.

Materials and Methods

CT scans acquired for various indications other than a suspicion of VCF from January 2019 to August 2020 were retrospectively and consecutively collected. The algorithm was designed to label each vertebra, detect VCF, measure vertebral height loss (VHL) and calculate mean Hounsfield Units (mean HU) for vertebral bone attenuation. For the ground truth, two board-certified radiologists defined if VCF was present and performed the measurements. The algorithm analyzed the scans and the results were compared to the experts' assessments.

Results

A total of 100 patients (mean age: 76.6 years ± 10.1[SD], 72% women) were evaluated. The overall labeling agreement was 94.9% (95%CI: 93.7%–95.9%). Regarding VHL, the 95% limits of agreement (LoA) between the algorithm and the radiologists was [-9.3, 8.6]; 94.1% of the differences lay within the radiologists' LoA and the intraclass correlation coefficient was 0.854 (95%CI: 0.822–0.881). For the mean HU, Pearson's correlation was 0.89 (95%CI: 0.84–0.92; p-value <0.0001). Finally, the algorithm's VCF screening sensitivity and specificity were 92.3% (95%CI: 81.5%–97.9%) and 91.7% (95%CI: 80.0%–97.7%), respectively.

Conclusions

This automated tool for screening and quantification of opportunistic VCF demonstrated high reliability and performance that may facilitate radiologists' task and improve opportunistic osteoporosis assessments.