Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient’s spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.
This Rapid Evidence Review was commissioned by the Technology Working Group of the Royal Osteoporosis Society Osteoporosis and Bone Research Academy, to inform the Society’s 2020 Research Road Map and Cure Strategy (https://tinyurl.com/y6oaj46j). This article is drawn from an initial evidence review undertaken by CM (Health Evidence Matters Ltd), which was supported by a grant from the Royal Osteoporosis Society. KESP is supported by the NIHR Cambridge Biomedical Research Centre (BRC).The full review was summarised for scientific publication by VA and KESP. The authors are grateful to Caroline Sangan, Belinda Thompson and Francesca Thompson for their assistance in convening the Working Group, whose scientific membership comprises: KESP (Chair), EMC (Vice-Chair), RLA, PB, PAB, NC, JSG, EPK, NCH, KAW and JEC (as Academy Chair). The authors are especially grateful to the Royal Osteoporosis Society Patient Advocates for their contributions to the group; Mary Bishop, Lois Ainger, Nic Vine and Karen Whitehead.
- artificial intelligence
- computed tomography
- fragility fracture
- VERTEBRAL FRACTURE