Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation

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.


Introduction
With modern computed tomography (CT) scans, some portion of the patients' spine is visualised in detail during ordinary chest, abdomen and pelvis scanning, giving ample opportunity for diagnosing osteoporosis and for various methods of vertebral fracture assessment (VFA) technologies. These range from manual identification right through to semi-automated and fully automated methods, some of which are accepted for diagnosis by international specialist societies. A summary of products and services available to measure bone health in the CT-attending population is provided in Figure 1, highlighting the niches they occupy in typical primary and secondary osteoporosis screening strategies. This review focus not only on the technologies, but also on the barriers to their adoption.
Artificial intelligence (AI), along with its sub-disciplines of machine learning (ML) and deep learning (DL) are emerging as key technologies with the potential to improve patient outcomes. ML is a set of software algorithms and statistical models used to perform a specific task, without using explicit instructions. This approach is different from the other types of software we review, where products have emerged from coding done intentionally (based on what developers already know about proven osteoporosis predictors). With AI, large data sets of CT images are coupled with knowledge of eventual fracture outcomes and prevalence to 'learn' which imaging features predict the outcome of interest.
This Rapid Review aims to provide a comprehensive review of the topic but is not a full systematic review of all related literature. Cochrane guidance on Rapid Review methodology was published recently (https://tinyurl.com/y6ce5g4v). For indepth evaluation of the technical CT methodologies, we recommend two recent review papers. 1,2 Figure 1. Comparison of available products and services (i-vi) to measure bone health in the CT-attending population, their place in screening and the barriers to adoption in a health service (dashed grey horizontal lines). A large proportion of older patients have previously undiagnosed osteoporosis (left panel), and some even have previously undiagnosed vertebral fractures (with or without osteoporosis). Starting with all older patients attending for routine CT, there are tools to screen all scans (Optasia ASPIRE and Zebra AI1) to identify possible vertebral fractures. Other tools (Mindways QCT Pro and VirtuOst) are best suited to some form of fracture risk assessment, with higher-risk individual scans being selected for analysis of density, strength and vertebral fracture (depending on the system).

Definition of 'approved' software and services
This review considers all technologies that have either received United States (US) Food and Drug Administration (FDA) approval, ISO 13485 certification (in the case of Medical Devices that involve a phantom), a European CE mark for diagnosis, or are National Health Service (NHS) Care Quality Commission (CQC) regulated technology services. We also evaluated studies showing the cost effectiveness of the use of CT technologies. We consider each technology, its mechanism, integration within clinical systems and the evidence for its efficacy.

Patient and public involvement
The Patient and Public team of the Royal Osteoporosis Society conducted a survey of members seeking their views on different research questions through their Bone Academy Patient Insight Group (December 2019-January 2020). In total, 2313 patient responders with osteoporosis (from 7237 mailed) graded priorities and expectations from 13 key areas across the domains of Osteoporosis Causes, Technology and Service Effectiveness. Opportunistic detection of osteoporosis and vertebral fractures from CT data was the highest ranking priority, with 70% of patients thinking that patients were 'extremely likely to benefit' from the idea and a further 22% 'very likely to benefit' (92% score in total).

Data sources
Data sources searched include: • NICE Evidence library portal; • Systematic reviews via: Cochrane Library;

Osteoporosis and vertebral fragility fractures
Osteoporosis is a disease that causes bones to become weak and fragile. It is a major cause of disability, loss of quality of life and early death in the older population and poses a significant public health problem in a globally ageing population. The condition is usually asymptomatic until a fracture occurs, and patient perception of fracture risk is often underestimated. 3 Vertebral fragility fractures occur either spontaneously, as a result of normal activities such as lifting or coughing, or from mild trauma. These spinal fractures are the most common of all osteoporotic fragility fractures, occurring in 25% of men and postmenopausal women. 4 Under-diagnosis is a particular issue for vertebral fractures as only a minority result from a fall and symptoms may be attributed by both patients and clinicians to another cause. 5 Nearly all fractures are associated with an increased risk of future fracture, 4 journals.sagepub.com/home/tab regardless of age, bone mineral density (BMD) and fracture location. 6 According to the International Osteoporosis Foundation (IOF), 520,000 fragility fractures occurred in 2017 in the United Kingdom (UK), costing £4.5 billion. This expenditure is set to increase by 30% by 2030 due to the ageing population. [7][8][9][10] Treatment and behavioural interventions for people diagnosed with osteoporosis and vertebral fractures have been shown to reduce hip and other fracture rates by 40-70%. 11 A recent comprehensive review has found that secondary prevention strategies appear to be better developed and more successful than primary prevention strategies. 12 However, currently less than half of patients with a fragility fracture undergo secondary osteoporosis screening. 13 This is a missed opportunity, since the 'lowest hanging fruit' in secondary prevention of fractures are those people presenting to secondary care with a first fragility fracture. This group includes people found to have an incidental vertebral fracture after a CT scan, which is the subject of this evidence review.

The role of fracture liaison services
The failure to treat osteoporosis even after knowledge of a fragility fracture is known as the 'osteoporosis treatment gap'. The percentage of women who did not receive treatment after a fracture was estimated by the IOF to be 49%. 9,10 Despite proven efficacy of osteoporosis therapy, simple guidelines and multiple simple therapeutic options, treatment prescription rates remain sub-optimal. 14 Clinical care systems have been slow to incorporate secondary prevention. The usual care following a lowtrauma fracture (including hip and vertebral) can still lack a simple evaluation and/or treatment of the osteoporosis that contributed to the fracture. 15 A multidisciplinary fracture liaison service (FLS) can facilitate case identification, investigation and intervention, 16 reducing the osteoporosis treatment gap and preventing fractures. [17][18][19][20] Their effectiveness at reducing the risk of subsequent fracture is supported by level 1 evidence from systematic reviews with meta-analyses. 21,22 Currently, it is unusual for FLS to follow up patients whose vertebral fractures are identified 'opportunistically' during CT scanning for other reasons.
Osteoporosis case-finding during CT Patients attending hospital for routine CT are a group of patients who might be suitable for targeted case-finding. The Royal Osteoporosis Society and the Royal College of Radiologists (UK) have recently overseen education and audit initiatives focused on improving the identification of osteoporotic vertebral fractures in imaging done for other reasons, including CT. Their recent audit of UK radiology departments found that only 26% of vertebral fractures visualised incidentally on CT images were reported accurately, and less than 3% of patients were referred onwards for appropriate management (see Figure 1).  11,23 In the case of patients attending hospital for routine CT, these patients will often fulfil the NICE criteria that recommend a fracture risk assessment due to age and co-morbidities. However, CT-attenders are not currently targeted for risk assessment. Fracture risk assessment using the online FRAX tool to input simple questionnaire answers gives a person-specific 10-year risk of osteoporotic fracture. A 'FRAX 10-year risk' of major osteoporotic or hip fracture is therefore the commonest method used to identify individuals at high risk of fracture in both primary and secondary prevention. Diagnosing osteoporosis using DXA Dual energy X-ray absorptiometry (DXA) remains the traditional imaging technique in osteoporosis and gold standard for diagnosis. It uses an X-ray and detector system to measure the mineral content of bone and is especially well suited to the average lumbar spine (usually lumbar vertebrae 1 and 2 or 1, 2, 3 and 4) as well as the proximal femur [femoral neck (FN) and 'total hip']. The Word Health Organisation (WHO) definition of osteoporosis is based on a DXA measurement of BMD, deriving from evidence showing a clear link between lower BMD and increased fracture risk. 25 Diagnostic criteria use standard deviation (SD) scores of BMD related to peak bone mass in healthy young women, with osteoporosis being defined as a BMD T score of −2.5 or less and low bone mass (osteopenia) as a BMD T-score between −1 and −2.5. 26 DXA BMD values, particularly derived from the FN, are a very good indicator of future fracture risk and have long been incorporated into modern fracture risk estimating tools such as FRAX. DXA is subject to the limitations of a planar twodimensional (2D) technology to represent a three-dimensional (3D) bone, and availability is patchy. Another limitation of DXA spine measurements are the inaccuracies in the setting of degenerative spinal pathology, and that this measurement is limited only to the lumbar spine. Each T-score unit decrease in BMD confers approximately a doubling of fracture risk. However, most osteoporotic fractures occur in individuals who do not have an 'osteoporotic range' BMD. In addition, other risk factors (e.g. age, sex, previous fracture) are associated with fracture risk independently of BMD. 27

Opportunistic ancillary screening for osteoporosis, low bone strength and vertebral fractures in CT scans done for other indications
Practical aspects of ancillary screening of CT data According to the latest data from NHS England, almost 6 million CT scans were performed October 2019-October 2020 for patients in England. 32 Of these, over 1 million were estimated to include the chest and/or abdomen. If opportunistic ancillary screening was performed on these CT scans, earlier treatment for those with previously undetected osteoporosis might have saved and improved lives, and potentially saved significant costs to healthcare systems. Opportunistic CT-based screening methods have the potential to be light-touch (in terms of cost, time and inconvenience to stakeholders) and to prevent unnecessary hospital visits and further irradiation. 33 Academic researchers, software companies and service providers have realised the potential to diagnose osteoporosis and identify vertebral fractures as an 'added extra' service applied to CT scan images that have already been taken for other clinical reasons. A single clinical CT scan consists of a batch of hundreds of consecutive 2D slices (axial sections) through a person (the number of slices depending on the predetermined slice thickness and the amount of the body covered by the scan). The software and services for screening for osteoporosis and vertebral fractures are not usually installed on the radiographers' CT scanning personal computer (PC) that drives the CT scanner. Instead the bone analysis software can be located either on nearby 'standalone' PCs or on the radiologists' analysis terminal [called a picture archive and communication system (PACS) diagnostic workstation] or even at a totally distant site.
In the latter case, analysis can be done by technical staff rather than radiographers, and sometimes away from the hospital, as long as the organisation providing the service is CQC-approved by the NHS. Using computer software to diagnose osteoporosis or vertebral fractures can be done any time; from minutes to hours after the patient has left the CT department, up to many months after the original scan. Here, the extra radiation dose is zero and the patient may be spared additional DXA imaging visits. From a regulatory perspective, the CQC is the UK's independent regulator of health and social care. Their report from March 2020 highlighted a range of observations and recommendations. 48 They emphasised the need for good governance of clinical, information, technical and human aspects of any ML tools in diagnostic services. They stated that most suppliers of ML applications in diagnostics will not need to register with CQC, only those that deliver clinical activity themselves. These few will need to be regulated and assessed by national standards to ensure safety and efficacy. The report emphasised that  56 These data are further validated by a US retrospective casecohort study of 4000 participants, in which accuracy of the BMD T-score as measured by VirtuOst analysis was consistent with DXA for all fracturerisk metrics and both sexes. 57 Importantly, the use of VirtuOst could be vital in inflammatory bowel disease monitoring, where a study of 257 patients who underwent CTE and BCT showed 54.5% of patients had high/increased fracture risk, of which 40.3% did not meet any of the Cornerstone screening criteria (IBD checklist for monitoring and prevention in bone health). 58 The prospective diagnostic accuracy of 2D measurements of FN BMD (using QCT) for incident hip fracture was established recently using the VirtuOst method. 31 Average FN aBMD measured by QCT was highly statistically significantly associated with incident hip fracture; age-adjusted OR for incident hip fracture was 3. CliniQCT involves the FDA-approved and ISO 13485-approved 'asynchronous QCT calibration' method to analyse bone density in CT images from any scanner that has been calibrated by the Model 4 cylinder phantom. This enables the opportunistic use of CT data sets acquired for other purposes that did not include a CT calibration phantom in the patient images; a technical obstacle that is also overcome (albeit using a different method) by VirtuOst. Like VirtuOst, Mindways' DXA-equivalent CTXA hip module gives areal bone density values (in g/cm 2 ) and T-scores that are approved for diagnosis by the ISCD, as well as 3D volumetric analysis of BMD in the spine (in g/cm 3 ). Phantom scans maintain precision and account for drift. Mindways' CTXA data analysed in both a conventional and asynchronous manner confirmed diagnostic accuracy with excellent intra-and inter-reader reliability and correlation with DXA (r 2 = 0.907, r 2 = 0.82). 38,60-63 QCT Pro is different insofar it requires a Model 3 flattened, curved phantom to be placed under the patient during a dedicated hip and or spine scan, plus a separate QA phantom is fitted onto the Model 3 for monthly quality scans (or after a CT X-ray tube change).
Mindways software can be run on a standard PC and does not require radiology-specific monitors or computers. Unlike the other systems in section 4 of this review, end-users of CliniQCT or QCT Pro typically retrieve eligible CT scans into their standard PC workstation from any PACS archive (at any time), perform the hip/spine analysis on a local copy of the CT scans and create a compliant clinical report. Alternatively, radiographers may decide to send CT scans from the actual CT scanning console to the Mindways DICOM server (i.e. both 'push' and 'pull' of CT images is supported). The software connects directly with any hospital PACS infrastructure to facilitate these retrieval and send/archive steps. Mindways software features a simple graphical user interface (GUI), guiding the user through bone analysis, creation of the report, printing and, if required, exporting the results back to the PACS archive to sit alongside the CT slices for all PACS users to see; analysis takes 2-3 min. The current Slicepick module displays anterior-posterior (AP) and lateral flattened composite spine images and contains basic measurement tools for identifying and confirming vertebral fracture by morphometry. The QCT Pro measurement of spine BMD (typically L1-L3 but supported from lower thoracic to lower lumbar spine with age and sex-matched reference data) has FDA approval, as does the CTXA method for diagnosing osteoporosis. As outlined above, the ACR threshold for spinal osteoporosis <80 mg/cm 3 ) is very strongly associated with prevalent and incident vertebral fracture.
Even more important than the diagnostic accuracy and utility of CTXA measurements of FN BMD are their clinical utility when imported into the FRAX tool. Thus patient-specific 10-year major and hip osteoporotic fracture risk augmented by FN BMD (the gold standard recommended by most national guidelines) can be achieved if patients first fill in the FRAX questionnaire before CT. Indeed, The FRAX tool BMD entry has a specific 'Mindways' category reflecting the acceptance of this way of measuring bone density by the ISCD and FRAX; inputting CTXA density to FRAX is possible in 66 countries worldwide at the time of writing. The feasibility of opportunistic screening for osteoporosis and vertebral fractures using CliniQCT and CTXA with FRAX is currently being tested in the PHOENIX study (ISCRTN 14722819, https:// doi.org/10.1186/ISRCTN14722819.)

Optasia medical
Optasia Medical specialises in software powered by ML algorithms that support the opportunistic case-finding of vertebral fracture patients. The Optasia Medical ASPIRE service out-sources the reporting of vertebral fractures visualised incidentally on CT, using a high degree of automation, combined with oversight from an in-house radiologist to improve the accuracy and efficiency of VF reporting. The service is already regulated by the CQC in the UK, and the technology has achieved CE-marking. Their technology, developed together with academic partners in University of Manchester, UK, provides a semi-automated quantitative vertebral morphometry devised from shape-based statistical modeling. [64][65][66][67][68][69] These are used to identify and grade vertebral fractures using output measurements including vertebral height measurements and ratios and vertebral fracture classifications. In a real-world test of the software capability on CT scout views, their earlier SpineAnalyzer software, applied to CT lateral scout views, provided good-excellent agreement with the standard radiologist grading for prevalent vertebral fractures, with excellent intra and inter-reader reliability (coefficients 0.96-0.98). 67 The ASPIRE software is designed to interface to a hospital PACS via a virtual machine running on a remote network. The software searches PACS for any relevant CT scans that include the spine and fulfil other criteria for example, patients >50 years of age. Identified scans are analysed and the output is reviewed by the radiologist who confirms or refutes the diagnosis, following which a report is automatically generated and returned to the requesting hospital site, the patients GP, and their local FLS or bone health team (Figure 2).
Retrospective feasibility studies involved a random sample of 1638 scans from five UK NHS Hospitals (Croydon, Cambridge, East Lancashire, Oxford and Salford). 70 Vertebral fractures were identified in 237 patients (14.2% ± 2.0). Only 67.7% of patients with vertebral fracture identified by the service had been found in the original radiology report, and only 13.3% of patients had been referred for appropriate management. In other feasibility studies of two different NHS sites (n = 7103), vertebral fractures were found in 20% of cases, of which 34% had been identified in radiology reports and 5.2% had been referred for appropriate management. As a result of the study, 1205 patients were referred by the service. These data were used to change practice in Croydon, where local physicians implemented a new reporting system to alert referrers so that when incidental fractures are found on CT they undertake a bone health review according to local pathways, ensuring timely assessment and treatment as appropriate. 71 Zebra Medical Vision Clinicians are keen to explore 'point of review' tools that alert the specialist radiologist that the CT scan they are reviewing has a prevalent vertebral fracture, 'red flags' for eventual future vertebral fracture or prevalent osteoporosis. Zebra Medical Vision (Zebra-Med), focuses on AI in medical imaging. [72][73][74] In May 2020, their software was FDA approved for opportunistic detection in osteoporosis. Zebra-Med analyses chest and abdominal CT scans using deep neural network technology: a combination of convolutional neural network and recurrent neural network technology. 75,76 These analyse data from the spine to analyse bone density and detect vertebral fractures. The software uses statistical and machine learning methods to identify vertebral fractures, to measure the minimal L1-L4 vertebral spine density or to emulate a lumbar spine DXA T-score. The latter DXA emulation approach is different to those listed in 4a and 4b and cannot be imputed to FRAX at present. The DXA emulation method could be run on 96.5% of CT scans in a very large cohort, whereas the method approximating L1-L4 minimal trabecular density could be run successfully on 62.3% of CT scans. 77 In addition, Zebra-Med has released another bone health application, based on DL, to automatically identify fractures. 78 The fracture-identifying component of Zebra's AI1 software could be run successfully on 84.3% of CT scans in the aforementioned cohort. The software extracts a virtual sagittal section visualising the spinal midplane and identifies VFs using ML algorithms. It outputs the probability that the volume contains a VF, and a heat map indicating the probable location of the VF in the sagittal image. In a singlesite 'real world' clinical implementation study involving thoracic CT scans from 1696 patients with a VF prevalence of 24%, the system achieved a sensitivity of 54%, specificity of 92% and accuracy of 83%. The radiologist or other clinician is tasked with confirming whether the algorithmic output is correct and, if so, to grade the fracture. 79 From 48,227 individuals (51.8% women) age 50-90, the Zebra-Med algorithms applied together showed non-inferiority to basic FRAX in assessing 5-year fracture risk, and slightly better sensitivity and positive predictive value (+2.4%, +0.7% respectively). A shortcoming is that the study used only the most basic FRAX from charts rather than the online calculator to derive FRAX estimates; this is therefore based on the number of risk factors rather than the actual individually weighted risk factors.
In a different study using chest and abdominal CT scans from 1000 patients, sensitivity, specificity and accuracy were 84%, 73% and 82% respectively. 80 Simulated T-scores for 1693 CT studies compared with DXA showed few false positives (n = 92) relative to true positives (n = 1444) but more false negatives (n = 212) compared with true negatives (n = 245.) Clinical applications have been implemented in Europe and the US and, in partnership with tele-diagnostic company Tererad Tech, have expanded Zebra's cloud-based DL analytics engine to more than 20 countries and 150 hospitals and healthcare organisations in India, Africa and Asia.

CT measurements of Hounsfield units
While there are many research studies evaluating CT HU for osteoporosis screening applications, the HU thresholds for consistent application across devices vary both with device and with protocol so it is not considered feasible to undertake a calibration exercise for each combination of device and protocol, meaning that such methods have not achieved clinical adoption and are unlikely to ever fulfil 'approved technology' status (see above for definitions). They are not dealt with further in this review.

Cost-effectiveness, futility and acceptability studies
For the implementation of opportunistic screening of CT for osteoporosis in a healthcare setting, understanding of its cost-effectiveness is vital, especially given the amount of work generated downstream for FLS and prescribers. It is also important to know in which patients screening would be futile. This is particularly true for opportunistic evaluation of CT; many routinely acquired CT scans are for patients with cancer or cancer-monitoring in whom mortality is higher than the general public.  from simple to complex, with more complex projects being associated with higher failure rates. The NASSS framework can be applied to technologies in health and care either prospectively, to guide design and implementation, or retrospectively, to learn from failure. A diverse range of technology-supported programmes has been tested using this framework. Failure is often linked with complexity across multiple NASSS domains, and 10 principles have been highlighted to help manage and minimise this complexity. Table 2 shows the application of these principles to the current osteoporosis challenge. Opportunistic screening for osteoporosis and vertebral fractures in CT comes up against the four well-recognised barriers to implementation of technology in the NHS. First, against poor communication and connectivity, which slows innovation across individuals and organisations due to the fragmented structure of UK health services. Second, against lack of evaluation by NICE of the complex new technology. Third, against a lack of funding to take technologies forward for implementation at scale, even after successful pilots. Finally, even well designed innovations require system changes that the NHS is simply unable to afford the time, money and staff to implement, despite clear evidence that these changes would bring major benefits in the long run. An Institute of Public Policy Research report concluded that whilst barriers can vary between different innovations, a number of common problems exist across most innovations, namely: complexity, culture and money. 87 Several organisations and reports have highlighted challenges in implementing novel technologies in the NHS and provided some guidance on how these can be overcome. These include The Nuffield Trust, The Kings Fund and the Institute for Public Policy Research. 88,89 Several initiatives and organisations exist that try to improve the process. The NHS Accelerated Access Collaboration, NHS Innovation Accelerator and its associated programmes support fast-track of innovations from idea to adoption and spread; evaluation of this organisation has shown effectiveness in scale-up and spread of innovations. 87,[91][92][93][94][95][96] In April 2020, in the NHS Long Term Plan, a MedTech Funding Mandate was introduced as part of the wider strategy to accelerate the uptake of NICE-approved cost-saving MedTech products in the NHS. 97-99 Evidence shows that nationally managed schemes resulted in a more rapid and complete uptake compared with devices that were not part of a national programme. 100 In February 2019, NHSX was established as a government unit that is responsible for setting consistent national policy and developing best practice for technology, digital services and data throughout the NHS. 101

Discussion: areas for further research and development
Ancillary screening of CT data for osteoporosis and vertebral fractures is well supported by numerous academic papers focussed on software development and successful use in clinical practice. Various tools can now provide a rapid and reproducible screening method for osteoporosis and previously unidentified fractures. However, there are areas where further research is needed in order to address evidence gaps. It is currently unclear which patient groups should be included in opportunistic screening. It could be used exclusively in older adults, or also include other highrisk groups. A large proportion of routine CT attenders have specific co-morbidities, such as cancer, in comparison with the general population. Thus, while they have a higher unmet osteoporosis burden, the effects of screening, treatment and survival in these attenders needs to be understood in order to ascertain its clinical impact and cost-effectiveness.
It is yet to be determined how opportunistic CT imaging could be clinically integrated with current diagnostic methods. Determining whether it would be used in addition to, or instead of DXA, and for screening and/or definitive diagnosis, remains to be established. Further data will be required on which site(s) should be primarily used in opportunistic CT screening; for example, regions within the lumbar spine or hip and, if so, which sub-regions. The additional value of measuring hip and spine bone strength with CT FEA (to diagnose and treat patients on the basis of FBS) over simpler QCT methods needs to be quantitated. There are also no head-to-head studies providing comparative data that assess whether technologies that detect fracture are more effective than those assessing bone density/ bone strength, or comparing these technologies with any other methods of opportunistic screening.
Further understanding of the technology itself will be key to its widespread implementation. Each of the different calibration techniques has advantages and pitfalls; and additional research is necessary to characterise the sources of variation between scans using each calibration technique. In addition, the exact effect of IV contrast on the accuracy of the data is not yet known.
There are several service delivery issues. Should the services be standalone outside the NHS, embedded as 'point of care' or near 'point of care' tools and when should CT data be 'sent' for screening? A key emerging issue is the ability for healthcare providers to manage the higher workload resulting from increased case-finding. FLS and other healthcare providers could potentially be required to consult, administer treatment, follow up and monitor vastly increased numbers of affected patients. Local systems for service delivery would need to be established. These include logistics of how relevant diagnostic images would be stored, transmitted to healthcare providers (HCPs) and FLS teams, and how follow-up measurements, for instance, with DXA, would be comparable for the purposes of monitoring or drug-cessation.

Conclusion
Osteoporosis imposes a significant public health impact, as well as cost burden, and is increasing in prevalence. It remains under-diagnosed and under-treated. There is evidence from the literature to support multiple technologies using opportunistic screening of CT scans done for other indications, which could increase the rates of diagnosis, and therefore treatment to prevent fractures. There are still areas where further research is needed. However several barriers remain to the implementation of technologies into healthcare systems; encompassing problems with culture, complexity and funding. With further research and the use of new and existing initiatives, there may be opportunities for the implementation of these technologies into clinical practice.