Skip to main content

Advertisement

Log in

AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis

  • Practice
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms.

Methods

Markov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care (“treat none”; no intervention regardless of opportunistic CT findings), (2) universal statin therapy (“treat all” for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts.

Results

For the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the “treat none” and “treat all” strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario.

Conclusion

AI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022;303(2):241–254 (In Eng.). DOI: https://doi.org/10.1148/radiol.211561.

  2. Pickhardt PJ, Graffy PM, Perez AA, Lubner MG, Elton DC, Summers RM. Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value. Radiographics 2021;41(2):524–542 (In Eng.). DOI: https://doi.org/10.1148/rg.2021200056.

  3. Pickhardt PJ, Graffy PM, Zea R, et al. Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults. Radiology 2020;297(1):64–72 (In Eng.). DOI: https://doi.org/10.1148/radiol.2020200466.

  4. Pickhardt PJ, Graffy PM, Zea R, et al. Automated CT Biomarkers for Opportunistic Prediction of Future Cardiovascular Events and Mortality in an Asymptomatic Screening Population: A Retrospective Cohort Study. Lancet Digit Health 2020;2(4):E192–E200. (Article) (In Eng.). DOI: https://doi.org/10.1016/s2589-7500(20)30025-x.

  5. Siris ES, Baim S, Nattiv A. Primary Care Use of FRAX: Absolute Fracture Risk Assessment in Postmenopausal Women and Older Men. Postgrad Med 2010;122(1):82–90 (In Eng.). DOI: https://doi.org/10.3810/pgm.2010.01.2102.

  6. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2019;140(11):e596–e646 (In Eng.). DOI: https://doi.org/10.1161/cir.0000000000000678.

  7. Graffy PM, Liu J, O'Connor S, Summers RM, Pickhardt PJ. Automated Segmentation and Quantification of Aortic Calcification at Abdominal CT: Application of a Deep Learning-Based Algorithm to a Longitudinal Screening Cohort. Abdom Radiol 2019;44(8):2921–2929 (In Eng.). DOI: https://doi.org/10.1007/s00261-019-02014-2.

  8. Graffy PM, Liu J, Pickhardt PJ, Burns JE, Yao J, Summers RM. Deep Learning-Based Muscle Segmentation and Quantification at Abdominal CT: Application to a Longitudinal Adult Screening Cohort for Sarcopenia Assessment. Br J Radiol 2019:20190327 (In Eng.). DOI: https://doi.org/10.1259/bjr.20190327.

  9. Pickhardt PJ, Lee SJ, Liu JM, et al. Population-Based Opportunistic Osteoporosis Screening: Validation of a Fully Automated CT Tool for Assessing Longitudinal BMD Changes. Br J Radiol 2019;92(1094). DOI: https://doi.org/10.1259/bjr.20180726.

  10. Criqui MH, Denenberg JO, McClelland RL, et al. Abdominal Aortic Calcium, Coronary Artery Calcium, and Cardiovascular Morbidity and Mortality in the Multi-Ethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol 2014;34(7):1574–1579 (In Eng.). DOI: https://doi.org/10.1161/atvbaha.114.303268.

  11. Janjua SA, Massaro JM, Chuang ML, D'Agostino RB, Hoffmann U, O'Donnell CJ. Thresholds for Abdominal Aortic Calcium That Predict Cardiovascular Disease Events in the Framingham Heart Study. JACC Cardiovasc Imaging 2021;14(3):695–697 (In Eng.). DOI: https://doi.org/10.1016/j.jcmg.2020.09.019.

  12. Jurgens PT, Carr JJ, Terry JG, Rana JS, Jacobs DR, Jr., Duprez DA. Association of Abdominal Aorta Calcium and Coronary Artery Calcium with Incident Cardiovascular and Coronary Heart Disease Events in Black and White Middle-Aged People: The Coronary Artery Risk Development in Young Adults Study. J Am Heart Assoc 2021;10(24):e023037 (In Eng.). DOI: https://doi.org/10.1161/jaha.121.023037.

  13. O'Connor SD, Graffy PM, Zea R, Pickhardt PJ. Does Nonenhanced CT-based Quantification of Abdominal Aortic Calcification Outperform the Framingham Risk Score in Predicting Cardiovascular Events in Asymptomatic Adults? Radiology 2019;290(1):108–115 (In Eng.). DOI: https://doi.org/10.1148/radiol.2018180562.

  14. Roberts ET, Horne A, Martin SS, et al. Cost-Effectiveness of Coronary Artery Calcium Testing for Coronary Heart and Cardiovascular Disease Risk Prediction to Guide Statin Allocation: The Multi-Ethnic Study of Atherosclerosis (MESA). PLoS One 2015;10(3):e0116377 (In Eng.). DOI: https://doi.org/10.1371/journal.pone.0116377.

  15. Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults. Radiology 2019;291(2):360–367 (In Eng.). DOI: https://doi.org/10.1148/radiol.2019181648.

  16. Lee SJ, Pickhardt PJ. Opportunistic Screening for Osteoporosis Using Body CT Scans Obtained for Other Indications: the UW Experience. Clinical Reviews in Bone and Mineral Metabolism 2017;15(3):128-137. DOI: https://doi.org/10.1007/s12018-017-9235-7.

    Article  CAS  Google Scholar 

  17. Tosteson AN, Melton LJ, 3rd, Dawson-Hughes B, et al. Cost-Effective Osteoporosis Treatment Thresholds: The United States perspective. Osteoporos Int 2008;19(4):437–47 (In Eng.). DOI: https://doi.org/10.1007/s00198-007-0550-6.

  18. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and Economic Burden of Osteoporosis-Related Fractures in the United States, 2005–2025. J Bone Miner Res 2007;22(3):465–75 (In Eng.). DOI: https://doi.org/10.1359/jbmr.061113.

  19. Ettinger B, Black DM, Dawson-Hughes B, Pressman AR, Melton LJ, 3rd. Updated Fracture Incidence Rates for the US Version of FRAX. Osteoporos Int 2010;21(1):25–33 (In Eng.). DOI: https://doi.org/10.1007/s00198-009-1032-9.

  20. Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications. Annals of Internal Medicine 2013;158(8):588–595. (<Go to ISI>://WOS:000318062100015).

  21. Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC. Skeletal Muscle Cutoff Values for Sarcopenia Diagnosis Using T10 to L5 Measurements in a Healthy US Population. Sci Rep 2018;8:8. (Article) (In Eng.). DOI: https://doi.org/10.1038/s41598-018-29825-5.

  22. van der Werf A, Langius JAE, de van der Schueren MAE, et al. Percentiles for Skeletal Muscle Index, Area and Radiation Attenuation Based On Computed Tomography Imaging in a Healthy Caucasian Population. Eur J Clin Nutr 2018;72(2):288–296 (In Eng.). DOI: https://doi.org/10.1038/s41430-017-0034-5.

  23. Brugts JJ, Yetgin T, Hoeks SE, et al. The Benefits of Statins in People Without Established Cardiovascular Disease But with Cardiovascular Risk Factors: Meta-Analysis of Randomised Controlled Trials. BMJ 2009;338:b2376 (In Eng.). DOI: https://doi.org/10.1136/bmj.b2376.

  24. Tonelli M, Lloyd A, Clement F, et al. Efficacy of Statins for Primary Prevention in People at Low Cardiovascular Risk: A Meta-Analysis. CMAJ 2011;183(16):E1189–202 (In Eng.). DOI: https://doi.org/10.1503/cmaj.101280.

  25. Ward S, Lloyd Jones M, Pandor A, et al. A Systematic Review and Economic Evaluation of Statins for the Prevention of Coronary Events. Health Technol Assess 2007;11(14):1–160, iii–iv (In Eng.). DOI: https://doi.org/10.3310/hta11140.

  26. Alsheikh-Ali AA, Ambrose MS, Kuvin JT, Karas RH. The Safety of Rosuvastatin as Used in Common Clinical Practice: A Postmarketing Analysis. Circulation 2005;111(23):3051–3057 (In Eng.). DOI: https://doi.org/10.1161/circulationaha.105.555482.

  27. Zhang H, Plutzky J, Skentzos S, et al. Discontinuation of Statins in Routine Care Settings: A Cohort Study. Ann Intern Med 2013;158(7):526–534 (In Eng.). DOI: https://doi.org/10.7326/0003-4819-158-7-201304020-00004.

  28. Ridker PM, Pradhan A, MacFadyen JG, Libby P, Glynn RJ. Cardiovascular Benefits and Diabetes Risks of Statin Therapy in Primary Prevention: An Analysis from the JUPITER Trial. Lancet 2012;380(9841):565–71 (In Eng.). DOI: https://doi.org/10.1016/s0140-6736(12)61190-8.

  29. Pisu M, Kopperdahl DL, Lewis CE, Saag KG, Keaveny TM. Cost-Effectiveness of Osteoporosis Screening Using Biomechanical Computed Tomography for Patients with a Previous Abdominal CT. J Bone Miner Res 2019;34(7):1229–1239 (In Eng.). DOI: https://doi.org/10.1002/jbmr.3700.

  30. Yong JH, Masucci L, Hoch JS, Sujic R, Beaton D. Cost-Effectiveness of a Fracture Liaison Service—A Real-World Evaluation After 6 Years of Service Provision. Osteoporos Int 2016;27(1):231–40 (In Eng.). DOI: https://doi.org/10.1007/s00198-015-3280-1.

  31. McLellan AR, Gallacher SJ, Fraser M, McQuillian C. The Fracture Liaison Service: Success of a Program for the Evaluation and Management of Patients with Osteoporotic Fracture. Osteoporos Int 2003;14(12):1028–34 (In Eng.). DOI: https://doi.org/10.1007/s00198-003-1507-z.

  32. Black DM, Schwartz AV, Ensrud KE, et al. Effects of Continuing or Stopping Alendronate After 5 Years of Treatment: The Fracture Intervention Trial Long-term Extension (FLEX): A Randomized Trial. Jama 2006;296(24):2927–38 (In Eng.). DOI: https://doi.org/10.1001/jama.296.24.2927.

  33. MacLean C, Newberry S, Maglione M, et al. Systematic Review: Comparative Effectiveness of treatments to Prevent Fractures in Men and Women with Low Bone Density or Osteoporosis. Ann Intern Med 2008;148(3):197–213 (In Eng.). DOI: https://doi.org/10.7326/0003-4819-148-3-200802050-00198.

  34. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European Consensus on Definition and Diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39(4):412–23 (In Eng.). DOI: https://doi.org/10.1093/ageing/afq034.

  35. Beaudart C, Buckinx F, Rabenda V, et al. The Effects of vitamin D on Skeletal Muscle Strength, Muscle Mass, and Muscle Power: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Clin Endocrinol Metab 2014;99(11):4336–45 (In Eng.). DOI: https://doi.org/10.1210/jc.2014-1742.

  36. Darvishi A, Hemami MR, Shafiee G, et al. Sarcopenia Screening Strategies in Older People: A Cost Effectiveness Analysis in Iran. BMC Public Health 2021;21(1):926 (In Eng.). DOI: https://doi.org/10.1186/s12889-021-10511-7.

  37. Yanai H. Nutrition for Sarcopenia. J Clin Med Res 2015;7(12):926–31 (In Eng.). DOI: https://doi.org/10.14740/jocmr2361w.

  38. Fujita S, Volpi E. Nutrition and Sarcopenia of Ageing. Nutr Res Rev 2004;17(1):69–76 (In Eng.). DOI: https://doi.org/10.1079/nrr200481.

  39. Schoufour JD, Tieland M, Barazzoni R, et al. The Relevance of Diet, Physical Activity, Exercise, and Persuasive Technology in the Prevention and Treatment of Sarcopenic Obesity in Older Adults. Front Nutr 2021;8:661449 (In Eng.). DOI: https://doi.org/10.3389/fnut.2021.661449.

  40. Bruyère O, Reginster JY, Beaudart C. Lifestyle Approaches to Prevent and Retard Sarcopenia: A Narrative Review. Maturitas 2022;161:44–48 (In Eng.). DOI: https://doi.org/10.1016/j.maturitas.2022.02.004.

  41. Goates S, Du K, Arensberg MB, Gaillard T, Guralnik J, Pereira SL. Economic Impact of Hospitalizations in US Adults with Sarcopenia. J Frailty Aging 2019;8(2):93–99 (In Eng.). DOI: https://doi.org/10.14283/jfa.2019.10.

  42. Bruyère O, Beaudart C, Ethgen O, Reginster JY, Locquet M. The Health Economics Burden of Sarcopenia: A Systematic Review. Maturitas 2019;119:61–69 (In Eng.). DOI: https://doi.org/10.1016/j.maturitas.2018.11.003.

  43. Janssen I, Shepard DS, Katzmarzyk PT, Roubenoff R. The Healthcare Costs of Sarcopenia in the United States. J Am Geriatr Soc 2004;52(1):80–5 (In Eng.). DOI: https://doi.org/10.1111/j.1532-5415.2004.52014.x.

  44. Arias E, Xu JQ, Kochanek KD. United States Life Tables, 2016. National Vital Statistics Reports 2019;68(4).

  45. Lee KK, Cipriano LE, Owens DK, Go AS, Hlatky MA. Cost-Effectiveness of Using High-Sensitivity C-Reactive Protein to Identify Intermediate- and Low-Cardiovascular-Risk Individuals for Statin Therapy. Circulation 2010;122(15):1478–87 (In Eng.). DOI: https://doi.org/10.1161/circulationaha.110.947960.

  46. Haentjens P, Magaziner J, Colón-Emeric CS, et al. Meta-Analysis: Excess Mortality After Hip Fracture Among Older Women and Men. Ann Intern Med 2010;152(6):380–90 (In Eng.). DOI: https://doi.org/10.7326/0003-4819-152-6-201003160-00008.

  47. Administration SS. Red Book: A Summary Guide to Employment Supports for Persons with Disabilities Under the Social Security Disability Insurance and Supplemental Security Income Programs. In: Administration SS, ed. Washington, DC: US Government Printing Office, 2017.

  48. Radiology. ESo. The Cost of AI in Radiology: Is It Really Worth It? https://ai.myesr.org/healthcare/the-cost-of-ai-in-radiology-is-it-really-worth-it/

  49. Smith DH, Gravelle H. The Practice of Discounting in Economic Evaluations of Healthcare Interventions. Int J Technol Assess Health Care 2001;17(2):236–43 (In Eng.). DOI: https://doi.org/10.1017/s0266462300105094.

  50. Pletcher MJ, Pignone M, Earnshaw S, et al. Using the Coronary Artery Calcium Score to Guide Statin Therapy: A Cost-Effectiveness Analysis. Circ Cardiovasc Qual Outcomes 2014;7(2):276–84 (In Eng.). DOI: https://doi.org/10.1161/circoutcomes.113.000799.

  51. Ara R, Wailoo AJ. Estimating Health State Utility Values for Joint Health Conditions: A Conceptual Review and Critique of the Current Evidence. Med Decis Making 2013;33(2):139–53 (In Eng.). DOI: https://doi.org/10.1177/0272989x12455461.

  52. Beaudart C, Rizzoli R, Bruyère O, Reginster JY, Biver E. Sarcopenia: Burden and Challenges for Public Health. Arch Public Health 2014;72(1):45 (In Eng.). DOI: https://doi.org/10.1186/2049-3258-72-45.

  53. He N, Zhang Y, Zhang L, Zhang S, Ye H. Relationship Between Sarcopenia and Cardiovascular Diseases in the Elderly: An Overview. Front Cardiovasc Med 2021;8:743710 (In Eng.). DOI: https://doi.org/10.3389/fcvm.2021.743710.

  54. Zhang N, Zhu WL, Liu XH, et al. Prevalence and Prognostic Implications of Sarcopenia in Older Patients with Coronary Heart Disease. J Geriatr Cardiol 2019;16(10):756–763 (In Eng.). DOI: https://doi.org/10.11909/j.issn.1671-5411.2019.10.002.

  55. Park CH, Lee YT, Yoon KJ. Association Between Osteosarcopenia and Coronary Artery Calcification in Asymptomatic Individuals. Sci Rep 2022;12(1):2231 (In Eng.). DOI: https://doi.org/10.1038/s41598-021-02640-1.

  56. Shepherd J, Cobbe SM, Ford I, et al. Prevention of Coronary Heart Disease with Pravastatin in Men with Hypercholesterolemia: West of Scotland Coronary Prevention Study Group. N Engl J Med 1995;333(20):1301–7 (In Eng.). DOI: https://doi.org/10.1056/nejm199511163332001.

  57. Agten CA, Ramme AJ, Kang S, Honig S, Chang G. Cost-Effectiveness of Virtual Bone Strength Testing in Osteoporosis Screening Programs for Postmenopausal Women in the United States. Radiology 2017;285(2):506–517 (In Eng.). DOI: https://doi.org/10.1148/radiol.2017161259.

  58. Tengs TO, Wallace A. One Thousand Health-Related Quality-Of-Life Estimates. Med Care 2000;38(6):583–637 (In Eng.). DOI: https://doi.org/10.1097/00005650-200006000-00004.

  59. Alarid-Escudero F, Knowlton G, Easterly C, Enns E. “Decision Analytic Modeling Package (dampack).” R package version 4.0.3 (2020–10–10). https://github.com/DARTH-git/dampack.

  60. Pickhardt PJ, Summers RM, Garrett JW, et al. Opportunistic screening: radiology scientific expert panel. Radiology (submitted).

  61. Boltyenkov AT, Sanelli PC, Carlos RC, Eusemann CD. New Ways to Quantify the Value of Diagnostic Imaging in the Era of Value-Based Health Care. J Am Coll Radiol 2022;19(2 Pt A):240–242 (In Eng.). DOI: https://doi.org/10.1016/j.jacr.2021.10.006.

  62. Graffy PM, Sandfort V, Summers RM, Pickhardt PJ. Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment. Radiology 2019:190512 (In Eng.). DOI: https://doi.org/10.1148/radiol.2019190512.

  63. Lee SJ, Liu J, Yao J, Kanarek A, Summers RM, Pickhardt PJ. Fully Automated Segmentation and Quantification of Visceral and Subcutaneous Fat at Abdominal CT: Application to a Longitudinal Adult Screening Cohort. Br J Radiol 2018;91(1089):20170968 (In Eng.). DOI: https://doi.org/10.1259/bjr.20170968.

  64. Pickhardt PJ, Graffy PM, Zea R, et al. Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms. Am J Roentgenol 2021;216(1):85-92. DOI: https://doi.org/10.2214/ajr.20.23049.

    Article  Google Scholar 

  65. Pickhardt PJ, Jee Y, O'Connor SD, del Rio AM. Visceral Adiposity and Hepatic Steatosis at Abdominal CT: Association with the Metabolic Syndrome. AJR Am J Roentgenol 2012;198(5):1100–1107 (In Eng.). DOI: https://doi.org/10.2214/ajr.11.7361.

  66. Chalasani N, Younossi Z, Lavine JE, et al. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018;67(1):328–357 (In Eng.). DOI: https://doi.org/10.1002/hep.29367.

  67. Pickhardt PJ, Hahn L, del Rio AM, Park SH, Reeder SB, Said A. Natural History of Hepatic Steatosis: Observed Outcomes for Subsequent Liver and Cardiovascular Complications. American Journal of Roentgenology 2014;202(4):752-758. DOI: https://doi.org/10.2214/ajr.13.11367.

    Article  Google Scholar 

  68. Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021;301(2):250–262 (In Eng.). DOI: https://doi.org/10.1148/radiol.2021204288.

  69. Ajmera V, Park CC, Caussy C, et al. Magnetic Resonance Imaging Proton Density Fat Fraction Associates With Progression of Fibrosis in Patients with Nonalcoholic Fatty Liver Disease. Gastroenterology 2018;155(2):307–310.e2 (In Eng.). DOI: https://doi.org/10.1053/j.gastro.2018.04.014.

  70. Sanyal AJ, Van Natta ML, Clark J, et al. Prospective Study of Outcomes in Adults with Nonalcoholic Fatty Liver Disease. N Engl J Med 2021;385(17):1559–1569 (In Eng.). DOI: https://doi.org/10.1056/NEJMoa2029349.

  71. Corey KE, Klebanoff MJ, Tramontano AC, Chung RT, Hur C. Screening for Nonalcoholic Steatohepatitis in Individuals with Type 2 Diabetes: A Cost-Effectiveness Analysis. Dig Dis Sci 2016;61(7):2108–17 (In Eng.). DOI: https://doi.org/10.1007/s10620-016-4044-2.

  72. Pickhardt PJ, Perez AA, Garrett JW, Graffy PM, Zea R, Summers RM. Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes. AJR Am J Roentgenol 2021:1–8 (In Eng.). DOI: https://doi.org/10.2214/ajr.21.26486.

Download references

Funding

This study was supported in part by grants from Nano-X Imaging Ltd and Bracco Diagnostics; the funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Perry J. Pickhardt.

Ethics declarations

Conflict of interest

Dr. Pickhardt is an advisor to Nanox, Bracco, and GE Healthcare; Dr. Hassan is a consultant for Fujifilm, Olympus, Norgine, and Cosmo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pickhardt, P.J., Correale, L. & Hassan, C. AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis. Abdom Radiol 48, 1181–1198 (2023). https://doi.org/10.1007/s00261-023-03800-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-03800-9

Keywords

Navigation