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Artificial intelligence and spine imaging: limitations, regulatory issues and future direction

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Abstract

Background

As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered.

Purpose

The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research.

Methods

A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed.

Results

Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised.

Conclusions

Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.

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References

  1. Herzog RJ, Guyer RD, Graham-smith A, Simmons EDJ (1995) Contemporary concepts in spine care magnetic resonance imaging: use in patients with low back or radicular pain. Spine 20:1834–1838

    Article  CAS  Google Scholar 

  2. Lee BCP, Kazam E, Newman AD (1978) Computed tomography of the spine and spinal cord. Radiology 128:95–102. https://doi.org/10.1148/128.1.95

    Article  CAS  PubMed  Google Scholar 

  3. Carrino JA, Campbell PD Jr, Lin DC et al (2011) Effect of spinal segment variants on numbering vertebral levels at lumbar MR imaging. Radiology 259:196–202

    Article  Google Scholar 

  4. Fan S-W, Fang X-Q, Liu Y-J et al (2015) Reliability and variability in the interpretation of lumbar high intensity zone. Acta Orthop Traumatol Turc 49:606–613. https://doi.org/10.3944/AOTT.2015.14.0267

    Article  PubMed  Google Scholar 

  5. Hajiahmadi S, Shayganfar A, Askari M, Ebrahimian S (2020) Interobserver and intraobserver variability in magnetic resonance imaging evaluation of patients with suspected disc herniation. Heliyon 6:e05201. https://doi.org/10.1016/j.heliyon.2020.e05201

    Article  PubMed  PubMed Central  Google Scholar 

  6. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3:210–229. https://doi.org/10.1147/rd.33.0210

    Article  Google Scholar 

  7. Jamaludin A, Kadir T, Zisserman A (2017) SpineNet: Automated classification and evidence visualization in spinal MRIs. Med Image Anal 41:63–73. https://doi.org/10.1016/j.media.2017.07.002

    Article  PubMed  Google Scholar 

  8. Zhang D, Liu X, Shao M et al (2021) The value of artificial intelligence and imaging diagnosis in the fight against COVID-19. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-021-01522-7

    Article  Google Scholar 

  9. Golkov V, Dosovitskiy A, Sperl JI et al (2016) q-Space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging 35:1344–1351. https://doi.org/10.1109/TMI.2016.2551324

    Article  PubMed  Google Scholar 

  10. Jamaludin A, Fairbank J, Harding I et al (2020) Identifying scoliosis in population-based cohorts: automation of a validated method based on total body dual energy X-ray absorptiometry scans. Calcif Tissue Int 106:378–385. https://doi.org/10.1007/s00223-019-00651-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mehta SD, Sebro R (2020) Computer-aided detection of incidental lumbar spine fractures from routine dual-energy X-ray absorptiometry (DEXA) studies using a support vector machine (SVM) classifier. J Digit Imaging 33:204–210. https://doi.org/10.1007/s10278-019-00224-0

    Article  PubMed  Google Scholar 

  12. Han Z, Wei B, Leung S et al (2018) Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning. Neuroinformatics 16:325–337. https://doi.org/10.1007/s12021-018-9365-1

    Article  PubMed  Google Scholar 

  13. Niemeyer F, Galbusera F, Tao Y et al (2021) A deep learning model for the accurate and reliable classification of disc degeneration based on MRI data. Invest Radiol 56:78–85. https://doi.org/10.1097/RLI.0000000000000709

    Article  PubMed  Google Scholar 

  14. Galbusera F, Niemeyer F, Wilke H-J et al (2019) Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur Spine J 28:951–960. https://doi.org/10.1007/s00586-019-05944-z

    Article  PubMed  Google Scholar 

  15. Jimenez-Pastor A, Alberich-Bayarri A, Fos-Guarinos B et al (2020) Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data. Radiol Med 125:48–56. https://doi.org/10.1007/s11547-019-01079-9

    Article  PubMed  Google Scholar 

  16. Lessmann N, van Ginneken B, de Jong PA, Išgum I (2019) Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 53:142–155. https://doi.org/10.1016/j.media.2019.02.005

    Article  PubMed  Google Scholar 

  17. Harada GK, Siyaji ZK, Mallow GM et al (2021) Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the “RAD” risk profile. Eur Spine J 30:2167–2175. https://doi.org/10.1007/s00586-021-06866-5

    Article  PubMed  Google Scholar 

  18. Lee JH, Han IH, Kim DH et al (2020) Spine computed tomography to magnetic resonance image synthesis using generative adversarial networks : a preliminary study. J Korean Neurosurg Soc 63:386–396. https://doi.org/10.3340/jkns.2019.0084

    Article  PubMed  PubMed Central  Google Scholar 

  19. Langerhuizen DWG, Janssen SJ, Mallee WH, et al (2019) What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clin Orthop Relat Res® 477: 2482–2491

  20. Joshi RS, Haddad AF, Lau D, Ames CP (2019) Artificial intelligence for adult spinal deformity. Neurospine 16:686–694

    Article  Google Scholar 

  21. Harada GK, Siyaji ZK, Younis S et al (2020) Imaging in spine surgery: current concepts and future directions. Spine Surg Relat Res 4:99–110

    Article  Google Scholar 

  22. Louie PK, Harada GK, Sayari AJ et al (2020) Etiology-based classification of adjacent segment disease following lumbar spine fusion. HSS Jrnl 16:130–136. https://doi.org/10.1007/s11420-019-09723-w

    Article  Google Scholar 

  23. Cina A, Bassani T, Panico M et al (2021) 2-step deep learning model for landmarks localization in spine radiographs. Sci Rep 11:9482. https://doi.org/10.1038/s41598-021-89102-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. FDA Cleared AI Algorithms. https://models.acrdsi.org/. Accessed 21 Sep 2021

  25. Skelly AC, Dettori JR, Brodt ED (2012) Assessing bias: the importance of considering confounding. Evid Based Spine Care J 3:9–12. https://doi.org/10.1055/s-0031-1298595

    Article  PubMed  PubMed Central  Google Scholar 

  26. Badgeley MA, Zech JR, Oakden-Rayner L, et al (2018) Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables. [cs]

  27. Panesar A (2019) Machine learning and ai for healthcare: big data for improved health outcomes. Apress, Berkeley, CA

    Book  Google Scholar 

  28. Han H, Jiang X (2014) Overcome support vector machine diagnosis overfitting. Cancer Inform 13:145–158. https://doi.org/10.4137/CIN.S13875

    Article  PubMed  PubMed Central  Google Scholar 

  29. Assel M, Sjoberg DD, Vickers AJ (2017) The Brier score does not evaluate the clinical utility of diagnostic tests or prediction models. Diagn Progn Res 1:19. https://doi.org/10.1186/s41512-017-0020-3

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dankers FJWM, Traverso A, Wee L, van Kuijk SMJ (2019) Prediction modeling methodology. In: Kubben P, Dumontier M, Dekker A (eds) Fundamentals of clinical data science. Springer, Cham (CH)

  31. Reddy S, Fox J, Purohit MP (2019) Artificial intelligence-enabled healthcare delivery. J R Soc Med 112:22–28. https://doi.org/10.1177/0141076818815510

    Article  PubMed  Google Scholar 

  32. Banerjee I, Bhimireddy AR, Burns JL, et al (2021) Reading race: AI Recognises patient’s racial identity In: Medical Images. [cs, eess]

  33. Kruse CS, Goswamy R, Raval Y, Marawi S (2016) Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform 4:e38. https://doi.org/10.2196/medinform.5359

    Article  PubMed  PubMed Central  Google Scholar 

  34. He J, Baxter SL, Xu J et al (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25:30–36. https://doi.org/10.1038/s41591-018-0307-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6:94–98. https://doi.org/10.7861/futurehosp.6-2-94

    Article  PubMed  PubMed Central  Google Scholar 

  36. ISO - International Organization for Standardization. In: ISO. https://www.iso.org/home.html. Accessed 22 Aug 2021

  37. Han H (2015) Diagnostic biases in translational bioinformatics. BMC Med Genomics. https://doi.org/10.1186/s12920-015-0116-y

    Article  PubMed  PubMed Central  Google Scholar 

  38. De Feo JA (2017) Juran’s quality handbook: the complete guide to performance excellence, seventh edition, 7th edn. McGraw-Hill Education, New York

    Google Scholar 

  39. Kodra Y, Posada de la Paz M, Coi A et al (2017) Data quality in rare diseases registries. Adv Exp Med Biol 1031:149–164. https://doi.org/10.1007/978-3-319-67144-4_8

    Article  PubMed  Google Scholar 

  40. Scannapieco M, Missier P, Batini C (2005) Data Quality at a Glance. Datenbank-Spektrum 14:6–14

    Google Scholar 

  41. Sidi F, Shariat Panahy PH, Affendey LS, et al (2012) Data quality: a survey of data quality dimensions. In: 2012 International Conference on Information Retrieval Knowledge Management. pp 300–304

  42. Roccetti M, Delnevo G, Casini L, Salomoni P (2020) A cautionary tale for machine learning design: why we still need human-assisted big data analysis. Mobile Netw Appl 25:1075–1083. https://doi.org/10.1007/s11036-020-01530-6

    Article  Google Scholar 

  43. Gerke S, Babic B, Evgeniou T, Cohen IG (2020) The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. npj Digit Med 3:1–4. https://doi.org/10.1038/s41746-020-0262-2

    Article  Google Scholar 

  44. Health C for D and R (2021) Artificial intelligence and machine learning in software as a medical device. FDA

  45. Allen B (2019) The role of the FDA in ensuring the safety and efficacy of artificial intelligence software and devices. J Am Coll Radiol 16:208–210. https://doi.org/10.1016/j.jacr.2018.09.007

    Article  PubMed  Google Scholar 

  46. Babic B, Gerke S, Evgeniou T, Cohen IG (2019) Algorithms on regulatory lockdown in medicine. Science 366:1202–1204. https://doi.org/10.1126/science.aay9547

    Article  CAS  PubMed  Google Scholar 

  47. Ibrahim H, Liu X, Denniston AK (2021) Reporting guidelines for artificial intelligence in healthcare research. Clin Experiment Ophthalmol 49:470–476. https://doi.org/10.1111/ceo.13943

    Article  PubMed  Google Scholar 

  48. Cruz Rivera S, Liu X, Chan A-W et al (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26:1351–1363. https://doi.org/10.1038/s41591-020-1037-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cruz Rivera S, Liu X, Chan A-W et al (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health 2:e549–e560. https://doi.org/10.1016/S2589-7500(20)30219-3

    Article  PubMed  Google Scholar 

  50. Liu X, Cruz Rivera S, Moher D et al (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26:1364–1374. https://doi.org/10.1038/s41591-020-1034-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Liu X, Cruz Rivera S, Moher D et al (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2:e537–e548. https://doi.org/10.1016/S2589-7500(20)30218-1

    Article  PubMed  PubMed Central  Google Scholar 

  52. Liu X, Rivera SC, Moher D et al (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ 370:m3164. https://doi.org/10.1136/bmj.m3164

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sounderajah V, Ashrafian H, Aggarwal R et al (2020) Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med 26:807–808. https://doi.org/10.1038/s41591-020-0941-1

    Article  CAS  PubMed  Google Scholar 

  54. Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 351:h5527. https://doi.org/10.1136/bmj.h5527

    Article  PubMed  PubMed Central  Google Scholar 

  55. Collins GS, Moons KGM (2019) Reporting of artificial intelligence prediction models. The Lancet 393(10181):1577–1579. https://doi.org/10.1016/S0140-6736(19)30037-6

    Article  Google Scholar 

  56. Norgeot B, Glicksberg BS, Trupin L et al (2019) Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. JAMA Netw Open 2:e190606–e190606. https://doi.org/10.1001/jamanetworkopen.2019.0606

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH (2020) MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 27:2011–2015. https://doi.org/10.1093/jamia/ocaa088

    Article  PubMed  PubMed Central  Google Scholar 

  58. DECIDE-AI Steering Group (2021) DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat Med 27:186–187. https://doi.org/10.1038/s41591-021-01229-5

    Article  CAS  Google Scholar 

  59. Liberati A, Altman DG, Tetzlaff J et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ (Clinical research ed). https://doi.org/10.1136/bmj.b2700

    Article  Google Scholar 

  60. Ethics guidelines for trustworthy AI | Shaping Europe’s digital future. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai. Accessed 21 Sep 2021

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Rush University Medical Center, Orthopaedic Building, Suite 204-G, 1611 W. Harrison Street, Chicago, IL, 60612, USA

    Alexander L. Hornung, G. Michael Mallow, J. Nicolas Barajas, Alejandro A. Espinoza Orías, Matthew Colman, Frank M. Phillips, Howard S. An & Dino Samartzis

  2. University of Minnesota Medical School, Minneapolis, MN, USA

    Christopher M. Hornung

  3. Spine Center, Schulthess Klinik, Zurich, Switzerland

    Fabio Galbusera

  4. Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany

    Hans-Joachim Wilke

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  1. Alexander L. Hornung
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Correspondence to Dino Samartzis.

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Hornung, A.L., Hornung, C.M., Mallow, G.M. et al. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. Eur Spine J 31, 2007–2021 (2022). https://doi.org/10.1007/s00586-021-07108-4

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