Development and multi-center validation of a software program, Ofeye 3.0, for automated all-inclusive vertebral fracture detection with chest/abdominal CT images.

Background: Missing report for fragility vertebral fracture (VF) on chest/abdominal CT is common when the indication is not spine disorders. This represents a missed opportunity to alert patients to take preventive measures to improve bone health and prevent further severe fractures. In this study, we aim to develop a software program for automated detection of VF with existing chest/abdominal CT scans and validate its detection performance. Methods: An automated sagittal Central Slab reconstruction (CSR) method for CT axial images was developed. For reference VF reading Results: The automated CSR method showed advantages in demonstrating structural changes of the endplate and adjacent structures. For detecting VF in chest/abdominal CT scans, counting case-by-case and compared with the reference reading, the average performance of Ofeye 3.0 was accuracy 0.967, sensitivity 0.906, and specificity 0.986. Most of false negative or false positive cases were minimal or mild VF, or with image artifacts, or with VF close to the peripheral of CSR images. Conclusions: Despite the challenging requirements for the software to detect all-inclusive VF, our results compare favorably with other published automated VF detection models. The translational potential of this article: We developed a software program for automated all-inclusive VF detection on chest and/or abdominal CT image data and conducted a multi-center external validation study. This software is proved to have high VF detection precision. By alerting patients of the VFs likely related to osteoporosis and in turn the patients taking measures to prevent further fracture, the integration of this software into radiological practice will improve patient outcomes and reduce healthcare costs.