In the current issue of JBMR, Kolanu and colleagues evaluate a
computer-aided diagnostic (CAD) system designed to identify
osteoporotic vertebral fractures (VFs) visualized opportunistically
from computed tomography (CT) images.(1) The system, developed
by Zebra Medical Vision (Shefayim, Israel; www.zebramed.
com), extracts a virtual sagittal section visualizing the spinal
midplane and identifies VFs using machine-learning algorithms.
It outputs the probability that the volume contains a VF and a
heat map indicating the probable locations of VFs in the sagittal
image. In a single-site study involving thoracic CT scans from
1696 patients with a VF prevalence of 24%, the system achieved
a sensitivity, specificity, and accuracy of 54%, 92%, and 83%,
respectively.None. Acknowledged Cambridge NIHR Biomedical Research Centr

Bromiley, Paul A

Clark, Emma M

Poole, Kenneth E

Poole, Kenneth E S

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                          Bromiley, P. A., Clark, E. M., & Poole, K. E. S. (2020). Computer-aided Diagnostic Systems for Osteoporotic Vertebral FractureDetection: Opportunities and Challenges. Journal of Bone and MineralResearch, 35(12), 2305-2306. https://doi.org/10.1002/jbmr.4205Peer reviewed versionLink to published version (if available):10.1002/jbmr.4205Link to publication record in Explore Bristol ResearchPDF-documentThis is the author accepted manuscript (AAM). The final published version (version of record) is available onlinevia Wiley at  https://doi.org/10.1002/jbmr.4205. Please refer to any applicable terms of use of the publisher.University of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only thepublished version using the reference above. Full terms of use are available:http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/  Computer-aided Diagnostic Systems for Osteoporotic Vertebral Fracture Detection: Opportunities and Challenges  P.A. Bromiley, E.M. Clark and K.E.S Poole  In the current issue of JBMR, Nithin Kolanu and colleagues evaluate a computer-aided diagnostic (CAD) system designed to identify osteoporotic vertebral fractures (VFs) visualised opportunistically from CT images.  The system, developed by Zebra Medical Vision (Shefayim, Israel; www.zebra-med.com), extracts a virtual sagittal section visualising the spinal mid-plane and identifies VFs using machine learning algorithms.  It outputs the probability that the volume contains a VF, and a heat map indicating the probable locations of VFs in the sagittal image.  In a single-site study involving thoracic CT scans from 1696 patients with a VF prevalence of 24%, the system achieved a sensitivity, specificity and accuracy of 54%, 92% and 83%, respectively.  Osteoporosis is under-diagnosed and under-treated in clinical practice (1).   While VFs are the most common osteoporotic fracture, they can be clinically "silent" despite being a crucial early clinical manifestation of the disease. This creates a potential role for opportunistic assessment for VFs in clinical images acquired for other indications, with CT arguably presenting the most important target.  A large and increasing number of procedures are performed and they frequently visualise part of the spine, offering the possibility of identifying a subset of the currently undiagnosed osteoporosis patients at little additional cost.  However, current diagnosis rates for CT are significantly worse than other modalities; a recent, UK-wide audit found that VFs visualised opportunistically in CT images were accurately reported in only 26.2% of patients, and only 2.6% were referred for further management (2).   The feasibility of improvement through existing radiology service provision is questionable.  CT imaging activity has increased rapidly in recent   years, rising by 110% between 2000 and 2016 in the US and by 69% in the UK NHS between 2012/13 and 2018/19 (3,4).    In contrast, the NHS currently has a shortfall of 1,876 radiologists, 33% of posts, forecast to rise to 43% by 2024 and only 1% of trusts were able to meet their reporting requirements within contracted hours in 2019 (5).   Similarly, the American Association of Medical Colleges has predicted a shortfall of between 17,100 and 41,900 physicians in specialities including radiology by 2033 (6).    CAD systems may provide a solution, with potential benefits for the patients in whom VFs are identified.  However, identification of the fracture is only part of the jigsaw puzzle, and integration into existing clinical workflows presents considerable challenges.  The current shortfall in radiologists militates against any solution that increases clinical workload.  Kolanu et al. show that Zebra’s CAD can identify images containing VFs, and highlight the probable location of the VF within the image, but the results are neither perfectly accurate nor complete.  The radiologist cannot yet rely on the system without thoroughly assessing the sagittal reformats, which takes precious reporting time.  The “elephant in the room” is that manual input from radiologists is still needed to confirm the diagnosis, identify the vertebral level, grade the severity of any fracture, and make recommendations for future management in line with local service provision in order to complete a radiology report that complies with clinical guidelines (7). [Compston2017]  Other CAD-based VF detection methods for clinical CT images are emerging, including those of Infervision (Beijing, China; global.infervision.com), O.N. Diagnostics (Berkeley, California; www.ondiagnostics.com), and Optasia Medical Ltd. (Manchester, UK; www.optasiamedical.com).  Systems that estimate bone mineral density (BMD) from CT, such as CliniQCT (Mindways Inc, Austin, Texas; www.qct.com) or that combine BMD assessment with bone strength analysis, such as VirtuOst (O.N. Diagnostics, Berkley, California; ondiagnostics.com), are also available.  In   contrast to the Zebra and Infervision systems, Optasia Medical's ASPIRE™ operates as a tele-radiology service, out-sourcing the opportunistic reporting of VFs with an in-house radiologist confirming the diagnoses, thus removing workload from radiology departments.  Nevertheless, these systems all have the potential to significantly increase the number of VFs identified, and thus impact downstream services, a factor which must be considered at the point of adoption.  CAD systems will have utility only if they can be incorporated into clinical workflows without imposing unrealistic burdens.  This requires consideration of multiple issues and potential technological interventions, of which improved VF diagnostic rates are only one.  Integration into existing radiology services must avoid increasing workload, and subsequent patient management must similarly avoid overwhelming the capacity of downstream services.  CAD systems should therefore produce actionable radiology reports that minimise the requirement for manual input or further diagnostic testing.  Computerised patient management systems that automate routine tasks, such as scheduling of patient contact and communications with primary care, might mitigate the impact of increased patient numbers.  It is also important to remember the ultimate aim; VF detection has clinical utility only if it enables prevention of future fractures through appropriate prescription of medications to reduce fracture risk and high enough patient adherence with medication regimes.  A retrospective study of Zebra's CAD system on a cohort of 48,227 patients found that fracture risk prediction using CT-based BMD estimation and VF detection was only slightly better than that provided by FRAX (www.shef.ac.uk/FRAX), which estimates fracture risk based on clinical factors, without BMD estimation (8).    CAD systems may have clinical utility, even without increasing diagnostic accuracy, if they automate fracture risk estimation while reducing clinical workload.  However, careful attention to health economics is required.  Studies have been performed for specific interventions, such as   bisphosphonate drugs (9).  However, these are insufficient to model the full impact of appropriate patient management and will underestimate patient benefit unless the full range standard interventions, such as falls risk assessments, are also considered.  Finally, adherence and persistence for first-line osteoporosis drugs are relatively poor (9).  Long-term patient support and monitoring are required to ensure persistence rates sufficient to reduce fracture risk.  Simple technologies such as mobile telephone apps that facilitate communication between patients, clinicians and patient support groups could therefore be highly effective.  In conclusion, a future where patients can benefit from machine-learning based diagnostic systems in the diagnosis and management of osteoporosis seems increasingly likely.  CAD systems with a high sensitivity and specificity for VFs are one facet of a completely novel clinical pathway, where technological support at multiple points will be required.  Conflicts of Interest  Dr. Bromiley has collaborated with Optasia Medical Ltd. on the development of the ASPIRE™ service, but has no financial interest in the company or service.  References  1. Akesson K, Marsh D, Mitchell PJ et al. Capture the Fracture: a Best Practice Framework and global campaign to break the fragility fracture cycle. Osteoporos Int. 2013;24(8):2135–52.     2. Howlett DC, Drinkwater KJ, Mahmood N, Illes  J, Griffin J and Javaid K. Radiology reporting of osteoporotic vertebral fragility fractures on computed tomography studies: results of a UK national audit. Eur Radiol. 2020;30(9):4713-23.  3. Smith-Bindman R, Kwan ML, Marlow EC et al.  Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA. 2019;322(9):843-56.  4. The National Health Service. Diagnostic Imaging Dataset Statistical Release 2018/2019. December 2019.  Available online at: www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2019/12/Annual-Statistical-Release-2018-19-PDF-1.9MB.pdf. Accessed 11th September 2020.  5. The Royal College of Radiologists. Clinical Radiology UK Workforce Census 2019 Report. April 2020. Available online at: www.rcr.ac.uk/system/files/publication/field_publication_files/clinical-radiology-uk-workforce-census-2019-report.pdf. Accessed 11th September 2020.  6. Association of American Medical Colleges. The Complexities of Physician Supply and Demand: Projections From 2018 to 2033. June 2020.  Available online at: www.aamc.org/system/files/2020-06/stratcomm-aamc-physician-workforce-projections-june-2020.pdf. Accessed 11th September 2020.  7. Compston J, Cooper A, Cooper C, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017;12(1):43.    8. Dagan N, Elnekave E, Barda N, et al. Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med. 2020;26:77–82.  9. Davis S, Martyn-St James M, Sanderson J, et al. A systematic review and economic evaluation of bisphosphonates for the prevention of fragility fractures. Health Technol Assess. 2016;20(78):1-406.      

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Computer-aided Diagnostic Systems for Osteoporotic Vertebral Fracture Detection:Opportunities and Challenges

The University of Manchester - Institutional Repository

The University of Manchester ResearchComputerAided Diagnostic Systems for OsteoporoticVertebral Fracture Detection: Opportunities andChallengesDOI:10.1002/jbmr.4205Document VersionFinal published versionLink to publication record in Manchester Research ExplorerCitation for published version (APA):Bromiley, P. A., Clark, E. M., & Poole, K. E. (2020). ComputerAided Diagnostic Systems for Osteoporotic VertebralFracture Detection: Opportunities and Challenges. Journal of Bone and Mineral Research.https://doi.org/10.1002/jbmr.4205Published in:Journal of Bone and Mineral ResearchCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:30. Jun. 2022EDITORIALComputer-Aided Diagnostic Systems for OsteoporoticVertebral Fracture Detection: Opportunities andChallengesI n the current issue of JBMR, Kolanu and colleagues evaluate acomputer-aided diagnostic (CAD) system designed to identifyosteoporotic vertebral fractures (VFs) visualized opportunisticallyfrom computed tomography (CT) images.(1) The system, devel-oped by Zebra Medical Vision (Shefayim, Israel; www.zebra-med.com), extracts a virtual sagittal section visualizing the spinalmidplane and identifies VFs using machine-learning algorithms.It outputs the probability that the volume contains a VF and aheat map indicating the probable locations of VFs in the sagittalimage. In a single-site study involving thoracic CT scans from1696 patients with a VF prevalence of 24%, the system achieveda sensitivity, specificity, and accuracy of 54%, 92%, and 83%,respectively.Osteoporosis is underdiagnosed and undertreated in clinicalpractice.(2) Although VFs are the most common osteoporoticfracture, they can be clinically “silent” despite being a crucialearly clinical manifestation of the disease. This creates a potentialrole for opportunistic assessment for VFs in clinical imagesacquired for other indications, with CT arguably presenting themost important target. A large and increasing number of proce-dures are performed, and they frequently visualize part of thespine, offering the possibility of identifying a subset of the cur-rently undiagnosed osteoporosis patients at little additional cost.However, current diagnosis rates for CT are significantly worsethan other modalities; a recent, UK-wide audit found that VFsvisualized opportunistically in CT images were accuratelyreported in only 26.2% of patients, and only 2.6% were referredfor further management.(3) The feasibility of improvementthrough existing radiology service provision is questionable. CTimaging activity has increased rapidly in recent years, rising by110% between 2000 and 2016 in the US and by 69% in the UKNHS between 2012–13 and 2018–19.(4,5) In contrast, the NHS cur-rently has a shortfall of 1876 radiologists, 33% of posts, forecastto rise to 43% by 2024, and only 1% of trusts were able to meettheir reporting requirements within contracted hours in 2019.(6)Similarly, the American Association of Medical Colleges has pre-dicted a shortfall of between 17,100 and 41,900 physicians inspecialties including radiology by 2033.(7)CAD systems may provide a solution, with potential benefitsfor the patients in whom VFs are identified. However, identifica-tion of the fracture is only part of the jigsaw puzzle, and integra-tion into existing clinical workflows presents considerablechallenges. The current shortfall in radiologists militates againstany solution that increases clinical workload. Kolanu andcolleagues show that Zebra’s CAD can identify images contain-ing VFs and highlight the probable location of the VF withinthe image, but the results are neither perfectly accurate nor com-plete. The radiologist cannot yet rely on the system without thor-oughly assessing the sagittal reformats, which takes preciousreporting time. The “elephant in the room” is that manual inputfrom radiologists is still needed to confirm the diagnosis, identifythe vertebral level, grade the severity of any fracture, and makerecommendations for future management in line with localservice provision in order to complete a radiology report thatcomplies with clinical guidelines.(8)Other CAD-based VF detection methods for clinical CTimages are emerging, including those of Infervision (Beijing,China; global.infervision.com), O.N. Diagnostics (Berkeley, CA,USA; www.ondiagnostics.com), and Optasia Medical Ltd.(Manchester, UK; www.optasiamedical.com). Systems that esti-mate bone mineral density (BMD) from CT, such as CliniQCT(Mindways Inc, Austin, TX, USA; www.qct.com) or that combineBMD assessment with bone strength analysis, such as VirtuOst(O.N. Diagnostics), are also available. In contrast to the Zebraand Infervision systems, Optasia Medical’s ASPIRE operates as atele-radiology service, outsourcing the opportunistic reportingof VFs with an in-house radiologist confirming the diagnoses,thus removing workload from radiology departments. Neverthe-less, these systems all have the potential to significantly increasethe number of VFs identified and thus impact downstream ser-vices, a factor that must be considered at the point of adoption.CAD systems will have utility only if they can be incorporatedinto clinical workflows without imposing unrealistic burdens.This requires consideration of multiple issues and potential tech-nological interventions, of which improved VF diagnostic ratesare only one. Integration into existing radiology services mustavoid increasing workload, and subsequent patient manage-ment must similarly avoid overwhelming the capacity of down-stream services. CAD systems should therefore produceactionable radiology reports that minimize the requirement formanual input or further diagnostic testing. Computerized patientmanagement systems that automate routine tasks, such asscheduling of patient contact and communications with primarycare, might mitigate the impact of increased patient numbers. Itis also important to remember the ultimate aim: VF detection hasclinical utility only if it enables prevention of future fracturesthrough appropriate prescription of medications to reduce frac-ture risk and high enough patient adherence with medicationReceived in original form September 13, 2020; revised form September 29, 2020; accepted October 7, 2020.Journal of Bone and Mineral Research, Vol. 00, No. 00, Month 2020, pp 1–2.DOI: 10.1002/jbmr.4205© 2020 American Society for Bone and Mineral Research (ASBMR)1 nregimes. A retrospective study of Zebra’s CAD system on a cohortof 48,227 patients found that fracture risk prediction using CT-based BMD estimation and VF detection was only slightly betterthan that provided by FRAX (www.shef.ac.uk/FRAX), which esti-mates fracture risk based on clinical factors, without BMDestimation.(9)CAD systems may have clinical utility, even without increasingdiagnostic accuracy, if they automate fracture risk estimationwhile reducing clinical workload. However, careful attention tohealth economics is required. Studies have been performed forspecific interventions, such as bisphosphonate drugs.(10) How-ever, these are insufficient to model the full impact of appropri-ate patient management and will underestimate patientbenefit unless the full range of standard interventions, such asfalls risk assessments, is also considered. Finally, adherence andpersistence for first-line osteoporosis drugs are relativelypoor.(10) Long-term patient support and monitoring are requiredto ensure persistence rates sufficient to reduce fracture risk.Simple technologies such as mobile telephone apps that facili-tate communication between patients, clinicians, and patientsupport groups could therefore be highly effective.In conclusion, a future where patients can benefit frommachine-learning-based diagnostic systems in the diagnosisand management of osteoporosis seems increasingly likely.CAD systems with a high sensitivity and specificity for VFsare one facet of a completely novel clinical pathway, wheretechnological support at multiple points will be required.DisclosuresPAB has collaborated with Optasia Medical Ltd. on the develop-ment of the ASPIRE service but has no financial interest in thecompany or service. EMC and KEP state that they have no con-flicts of interest.AcknowledgmentsAuthors’ roles: PAB, EMC, and KEP discussed and agreed uponthe contents of the article. PAB wrote the article with substantialinput from EMC and KEP. All authors reviewed and agreed uponthe final version.Author contributions: PAB: Writing-original draft; writing-review and editing. EMC: Writing-original draft; writing-reviewand editing. KEP: Writing-original draft; writing-review andediting.Paul A Bromiley PhDDivision of Informatics, Imaging, and Data Sciences, The Universityof Manchester, Manchester, UKEmma M Clark MBBS, PhDTranslational Health Sciences, Bristol Medical School, University ofBristol, Bristol, UKKenneth E Poole BM, PhDCambridge NIHR Biomedical Research Centre, Department ofMedicine, University of Cambridge, Cambridge, UKReferences1. Kolanu N, Silverstone EJ, Ho BH, et al. Clinical utility of computer-aided diagnosis of vertebral fractures from computed tomographyimages. J Bone Miner Res. 2020. Epub ahead of print. Available from:https://doi.org/10.1002/jbmr.4146.2. Akesson K, Marsh D, Mitchell PJ, et al. Capture the fracture: a bestpractice framework and global campaign to break the fragility frac-ture cycle. Osteoporos Int. 2013;24(8):2135–52.3. Howlett DC, Drinkwater KJ, Mahmood N, Illes J, Griffin J, Javaid K.Radiology reporting of osteoporotic vertebral fragility fractures oncomputed tomography studies: results of a UK national audit. EurRadiol. 2020;30(9):4713–23.4. Smith-Bindman R, Kwan ML, Marlow EC, et al. Trends in use of med-ical imaging in US health care systems and in Ontario, Canada,2000-2016. JAMA. 2019;322(9):843–56.5. The National Health Service. Diagnostic imaging dataset statisticalrelease 2018/2019. 2019. Available at: www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2019/12/Annual-Statistical-Release-2018-19-PDF-1.9MB.pdf. Accessed 2020 Sept 11.6. The Royal College of Radiologists. Clinical radiology UK workforcecensus 2019 report. 2020. Available at: www.rcr.ac.uk/system/files/publication/field_publication_files/clinical-radiology-uk-workforce-census-2019-report.pdf. Accessed 2020 Sept 11.7. Association of AmericanMedical Colleges. The complexities of physi-cian supply and demand: projections from 2018 to 2033. 2020. Avail-able at: www.aamc.org/system/files/2020-06/stratcomm-aamc-physician-workforce-projections-june-2020.pdf. Accessed 2020Sept 11.8. Compston J, Cooper A, Cooper C, et al. UK clinical guideline for theprevention and treatment of osteoporosis. Arch Osteoporos. 2017;12(1):43.9. Dagan N, Elnekave E, Barda N, et al. Automated opportunistic osteo-porotic fracture risk assessment using computed tomography scansto aid in FRAX underutilization. Nat Med. 2020;26:77–82.10. Davis S, Martyn-St James M, Sanderson J, et al. A systematic reviewand economic evaluation of bisphosphonates for the prevention offragility fractures. Health Technol Assess. 2016;20(78):1–406.Journal of Bone and Mineral Researchn 2 EDITORIAL

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Computer-Aided Diagnostic Systems for Osteoporotic Vertebral Fracture Detection: Opportunities and Challenges.

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