Software Platform for Automatic, Opportunistic Screening of Vertebral Compression Fractures

PROJECT SUMMARY / ABSTRACT Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. VCF burdens include, but are not limited to pain, functional impairment, increased risk of future fractures, medical costs and mortality. Only 1/3 of osteoporotic VCFs are symptomatic, and the remaining cases are found incidentally, thus, the burden of VCF disease is significantly underestimated. Despite this, around 700,000 osteoporotic VCFs are diagnosed annually in the US alone, resulting in an estimated annual economic burden of $13.8B. With an aging population, the rate of VCFs and its associated burdens are expected to rise. Therefore, it is of utmost importance to develop screening tools for VCF assessment and identifying individuals at risk of VCF. In this Direct Phase II SBIR project, BioSensics, in collaboration with Beth Israel Deaconess Medical Center will develop a cloud-based platform for automatic, opportunistic analysis of CT images that include spine. The solution will stand by in the central imaging data server of a hospital, investigate each non-investigated spine CT study, and flag the studies of patients that are detected to have osteoporosis or vertebra(e) at risk for fracture. The clinician providing care for the patient will then be prompted to consider ordering a screening for vertebral body compression fractures, bone mineral study or both, given the red flag from the analysis. Upon placing an order, a full report will be presented to the physician. This process is reimbursable under two Common Procedural Technology (CPT) codes that are relevant to the use of the proposed solution (CPT Code 77078 for bone mineral study using computed tomography, and Code 0X36T for reporting an automated analysis of an existing computed tomography study for vertebral fractures). The existing reimbursement CPT code and the significant added value of the proposed solution for hospitals and clinical institutions - in terms of generating additional direct revenue from using the solution as well as providing better care to patients at risk – will facilitate commercialization of the solution. In the longer term, the proposed imaging analysis technology can be used for automatic analysis of thousands of medical images that are taken every day in hospitals and clinics. This will enable detection of diseases and conditions at early stages (e.g., bone metastasis and different tumors), thus facilitating preventive measures and better care for those individuals at risk.