Skip to main content

Artificial Intelligence in Trauma and Orthopedics

  • Reference work entry
  • First Online:
Artificial Intelligence in Medicine

Abstract

This chapter will explore artificial intelligence (AI) in trauma and orthopedics (orthopedics). Orthopedics is a branch of surgery that focuses on the prevention of musculoskeletal pathology and the correction and restoration of form and function of these structures. Orthopedics is fertile ground for adoption of technological innovations, including artificial intelligence, where small gains in the treatment of one condition can lead to improved outcomes for some of the largest patient populations in medicine. Orthopedics is well suited to innovation and the application of AI as it has clear pathways for common diseases and is a highly technical field with constant technical innovation. This chapter will review several of the key applications of AI in orthopedics including diagnostics, intraoperative robotics, and predictive analytics.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Musculoskeletal conditions [Internet]. NHS: Long term conditions. [cited 2021 Feb 28]. https://www.england.nhs.uk/ourwork/clinical-policy/ltc/our-work-on-long-term-conditions/musculoskeletal/

  2. Watkins-Castillo S, Andersson G. United States Bone and Joint Initiative: the burden of musculoskeletal diseases in the United States (BMUS) [Internet]. The Burden of Musculoskeletal diseases in the United States. 2014. http://www.boneandjointburden.org

  3. Learmonth ID, Young C, Rorabeck C. The operation of the century: total hip replacement. Lancet. 2007;370:1508–19.

    PubMed  Google Scholar 

  4. NJR Centre. National Joint Registry: Home [Internet]. About. [cited 2021 Mar 3]. https://www.njrcentre.org.uk/njrcentre/default.aspx

  5. Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75.

    PubMed  PubMed Central  Google Scholar 

  6. Karl JW, Swart E, Strauch RJ. Diagnosis of occult scaphoid fractures a cost-effectiveness analysis. J Bone Jt Surg Am. 2014;97(22):1860–8.

    Google Scholar 

  7. Lau S, Bozin M, Thillainadesan T. Lisfranc fracture dislocation: a review of a commonly missed injury of the midfoot. Emerg Med J. 2017;34(1):52–6.

    PubMed  Google Scholar 

  8. Pinto A, Berritto D, Russo A, Riccitiello F, Caruso M, Belfiore MP, et al. Traumatic fractures in adults: missed diagnosis on plain radiographs in the emergency department. Acta Biomed. 2018;89:111–23.

    PubMed  PubMed Central  Google Scholar 

  9. Clementson M, Björkman A, Thomsen NOB. Acute scaphoid fractures: guidelines for diagnosis and treatment. EFORT Open Rev. 2020;5(2):96–103.

    PubMed  PubMed Central  Google Scholar 

  10. Nunley JA, Vertullo CJ. Classification, investigation, and management of midfoot sprains: Lisfranc injuries in the athlete. Am J Sports Med. 2002;30(6):871–8.

    PubMed  Google Scholar 

  11. Lee YH. Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks. J Digit Imaging. 2018;31(5):604–10.

    PubMed  PubMed Central  Google Scholar 

  12. Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic determination of the need for intravenous contrast in musculos- keletal MRI examinations using IBM Watson’s natural language processing algorithm. J Digit Imaging. 2018;31(02):245–51.

    PubMed  Google Scholar 

  13. Dutta S, Long WJ, Brown DFM, Reisner AT. Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings. Ann Emerg Med. 2013 Aug;62(2):162–9.

    PubMed  Google Scholar 

  14. Kunze KN, Rossi DM, White GM, Karhade AV, Deng J, Williams BT, et al. Diagnostic performance of artificial intelligence for detection of anterior cruciate ligament and meniscus tears: a systematic review. Arthrosc J Arthrosc Relat Surg [Internet]. 2021;37(2):771–81. https://doi.org/10.1016/j.arthro.2020.09.012.

    Article  Google Scholar 

  15. Diermeier T, Rothrauff BB, Engebretsen L, Lynch AD, Ayeni OR, Paterno MV, et al. Treatment after anterior cruciate ligament injury: Panther Symposium ACL Treatment Consensus Group. Knee Surg Sports Traumatol Arthrosc [Internet]. 2020;28(8):2390–402. https://doi.org/10.1007/s00167-020-06012-6.

    Article  Google Scholar 

  16. Stone JA, Perrone GS, Nezwek TA, Cui Q, Vlad SC, Richmond JC, et al. Delayed ACL reconstruction in patients ≥40 years of age is associated with increased risk of medial meniscal injury at 1 year. Am J Sports Med. 2019;47(3):584–9.

    PubMed  Google Scholar 

  17. Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, et al. Learning a variational network for reconstruction of accelerated MRI Data. arXiv. 2017.

    Google Scholar 

  18. Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P. Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends. Semin Musculoskelet Radiol. 2019;23(3):304–11.

    PubMed  Google Scholar 

  19. Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms – are they on par with humans for diagnosing fractures? Acta Orthop. 2017;88(6):581–6.

    PubMed  PubMed Central  Google Scholar 

  20. Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol [Internet]. 2018;73(5):439–45. https://doi.org/10.1016/j.crad.2017.11.015.

    Article  CAS  Google Scholar 

  21. Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. Saria S, editor. PLOS Med [Internet]. 2018 Nov 27 [cited 2021 Mar 3];15(11):e1002699. https://dx.plos.org/10.1371/journal.pmed.1002699

  22. Štajduhar I, Mamula M, Miletić DÜG. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Prog Biomed. 2017;140:151–64.

    Google Scholar 

  23. Chang PD, Wong TT, Rasiej MJ. Deep learning for detection of complete anterior cruciate ligament tear. J Digit Imaging. 2019;32(6):980–6.

    PubMed  PubMed Central  Google Scholar 

  24. Garwood ER, Tai R, Joshi G, Watts VGJ. The use of artificial intelligence in the evaluation of knee pathology. Semin Musculoskelet Radiol. 2020;24(1):21–9.

    PubMed  Google Scholar 

  25. Gorelik N, Chong J, Lin DJ. Pattern recognition in musculoskeletal imaging using artificial intelligence. Semin Musculoskelet Radiol. 2020;24(1):38–49.

    PubMed  Google Scholar 

  26. Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digit Imaging. 2019;32(3):471–7.

    PubMed  Google Scholar 

  27. Quatman CE, Hettrich CM, Schmitt LC, Spindler KP. The clinical utility and diagnostic performance of magnetic resonance imaging for identification of early and advanced knee osteoarthritis: a systematic review. Am J Sports Med. 2011;39(7):1557–68.

    PubMed  PubMed Central  Google Scholar 

  28. Wilson NA, Jehn M, York S, Davis CM. Revision total hip and knee arthroplasty implant identification: implications for use of unique device identification 2012 AAHKS member survey results. J Arthroplast. 2014;29(2):251–5.

    Google Scholar 

  29. Karnuta JM, Haeberle HS, Luu BC, Roth AL, Molloy RM, Nystrom LM, et al. Artificial intelligence to identify arthroplasty implants from radiographs of the hip. J Arthroplasty [Internet]. 2020; https://doi.org/10.1016/j.arth.2020.11.015.

  30. Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM. Detecting total hip replacement prosthesis design on preoperative radiographs using deep convolutional neural network. arXiv. 2019;1–16.

    Google Scholar 

  31. Lodwick G, Haun C, Smith W, Keller R, Robertson E. Computer diagnosis of primary bone tumors: a preliminary report. Radiology. 1963;80(2):273–5.

    Google Scholar 

  32. Ping YY, Yin CW, Kok LP. Computer aided bone tumor detection and classification using x-ray images. IFMBE Proc. 2008;21 IFMBE(1):544–7.

    Google Scholar 

  33. Bandyopadhyay O, Biswas A, Bhattacharya BB. Bone-cancer assessment and destruction pattern analysis in long-bone X-ray image. J Digit Imaging. 2019;32(2):300–13.

    PubMed  Google Scholar 

  34. Suhas MV, Mishra A. Classification of benign and malignant bone lesions on CT images using random forest. In: 2016 IEEE international conference on recent trends in electronics, information and communication technology, RTEICT 2016 – proceedings. 2017. p. 1807–10.

    Google Scholar 

  35. Lang JE, Mannava S, Floyd AJ, Goddard MS, Smith BP, Mofidi A, et al. Robotic systems in orthopaedic surgery. J Bone Jt Surg Ser B. 2011;93 B(10):1296–9.

    Google Scholar 

  36. Mavrogenis AF, Scarlat MM. Surgeons and robots. Int Orthop. 2019;43(6):1279–81.

    PubMed  Google Scholar 

  37. Huang H-M, Messina E, Albus J. Autonomy levels for unmanned systems (ALFUS) framework volume II: framework models version 1.0. 2007.

    Google Scholar 

  38. Picard F, Deakin AH, Riches PE, Deep K, Baines J. Computer assisted orthopaedic surgery: past, present and future. Med Eng Phys. 2019;72:55–65.

    PubMed  Google Scholar 

  39. Han X, Tian W, Liu Y, Liu B, He D, Sun Y, et al. Safety and accuracy of robot-assisted versus fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery: a prospective randomized controlled trial. J Neurosurg Spine. 2019;30(5):615–22.

    Google Scholar 

  40. Davies BL, Rodriguez Y, Baena FM, Barrett ARW, Gomes MPSF, Harris SJ, Jakopec M, et al. Robotic control in knee joint replacement surgery. Proc Inst Mech Eng H. 2007;221(1):71.

    CAS  PubMed  Google Scholar 

  41. Batailler C, Swan J, Marinier ES, Servien EL, Lustig S. New technologies in knee arthroplasty: current concepts. J Clin Med. 2021;10:47.

    Google Scholar 

  42. Pearle AD, O’loughlin PF, Kendoff DO. Robot-assisted unicompartmental knee arthroplasty. J Arthroplast. 2010;25(2):230.

    Google Scholar 

  43. St Mart JP, De Steiger RN, Cuthbert A, Donnelly W. The three-year survivorship of robotically assisted versus non-robotically assisted unicompartmental knee arthroplasty. Bone Jt J.2020;102 B(3):319–28.

    Google Scholar 

  44. Plate JF, Mofidi A, Mannava S, Smith BP, Lang JE, Poehling GG, et al. Achieving accurate ligament balancing using robotic-assisted unicompartmental knee arthroplasty. Adv Orthop. 2013;2013:1–6.

    Google Scholar 

  45. Chen AF, Kazarian GS, Jessop GW, Makhdom A. Current concepts review: robotic technology in orthopaedic surgery. J Bone Jt Surg Am. 2018;100(22):1984–92.

    Google Scholar 

  46. Kayani B, Konan S, Thakrar RR, Huq SS, Haddad FS. Assuring the long-term total joint arthroplasty: a triad of variables. Bone Jt J. 2019;101B(1):11–8.

    Google Scholar 

  47. Choong PF, Dowsey MM, Stoney JD. Does accurate anatomical alignment result in better function and quality of life? Comparing conventional and computer-assisted total knee arthroplasty. J Arthroplast. 2009;24(4):560–9.

    Google Scholar 

  48. Hiscox CM, Bohm ER, Turgeon TR, Hedden DR, Burnell CD. Randomized trial of computer-assisted knee arthroplasty: impact on clinical and radiographic outcomes. J Arthroplast. 2011;26(8):1259–64.

    Google Scholar 

  49. Christ AB, Pearle AD, Mayman DJ, Haas SB. Robotic-assisted unicompartmental knee arthroplasty: state-of-the art and review of the literature. J Arthroplast. 2018;33(7):1994–2001.

    Google Scholar 

  50. Kayani B, Konan S, Pietrzak JRT, Huq SS, Tahmassebi J, Haddad FS. The learning curve associated with robotic-arm assisted unicompartmental knee arthroplasty. Bone Jt J. 2018;100B(8):1033–42.

    Google Scholar 

  51. Jassim SS, Benjamin-Laing H, Douglas SL, Haddad FS. Robotic and navigation systems in orthopaedic surgery: how much do our patients understand? CiOs Clin Orthop Surg. 2014;6(4):642.

    Google Scholar 

  52. Kim Y-H, Yoon S-H, Park J-W. Does robotic-assisted TKA result in better outcome scores or long-term survivorship than conventional TKA? A randomized, controlled trial. Clin Orthop Relat Res [Internet]. 2020 Feb 1 [cited 2021 Mar 4];478(2):266–75. https://journals.lww.com/10.1097/CORR.0000000000000916

  53. Illgen RL, Bukowski BR, Abiola R, Anderson P, Chughtai M, Khlopas A, et al. Robotic-assisted total hip arthroplasty: outcomes at minimum two-year follow-up. Surg Technol Int. 2017;30:365–72.

    Google Scholar 

  54. Bukowski BR, Anderson P, Khlopas A, Chughtai M, Mont MA, Illgen RL. Improved functional outcomes with robotic compared with manual total hip arthroplasty. Surg Technol Int. 2016;29:303–8.

    PubMed  Google Scholar 

  55. Haddad FS, Horriat S. Robotic and other enhanced technologies: are we prepared for such innovation? Bone Jt J. 2019;101-B(12):1469–71.

    Google Scholar 

  56. Wyles CC, Tibbo ME, Fu S, Wang Y, Sohn S, Kremers WK, et al. Use of natural language processing algorithms to identify common data elements in operative notes for total hip arthroplasty. J Bone Jt Surg Am [Internet]. 2019 Nov 6 [cited 2021 Mar 3];101(21):1931–8. https://pubmed.ncbi.nlm.nih.gov/31567670/

  57. Pellisé F, Serra-Burriel M, Smith JS, Haddad S, Kelly MP, Vila-Casademunt A, et al. Development and validation of risk stratification models for adult spinal deformity surgery. J Neurosurg Spine [Internet]. 2019 Oct 1 [cited 2021 Mar 3];31(4):587–99. https://thejns.org/spine/view/journals/j-neurosurg-spine/31/4/article-p587.xml

  58. ESSG|Research Projects [Internet]. [cited 2021 Mar 3]. http://www.spine-essg.com/web/research-projects/research-awards/

  59. Jaremko JL, Poncet P, Ronsky J, Harder J, Dansereau J, Labelle H, et al. Estimation of spinal deformity in scoliosis from torso surface cross sections. Spine (Phila Pa 1976). 2001;26(14):1583–91.

    CAS  Google Scholar 

  60. Watanabe K, Aoki Y, Matsumoto M. An application of artificial intelligence to diagnostic imaging of spine disease: estimating spinal alignment from Moiré images. Neurospine. 2019;16:697–702.

    PubMed  PubMed Central  Google Scholar 

  61. Duong L, Cheriet F, Labelle H. Automatic detection of scoliotic curves in posteroanterior radiographs. IEEE Trans Biomed Eng. 2010;57(5):1143–51.

    PubMed  Google Scholar 

  62. Chen K, Zhai X, Sun K, Wang H, Yang C, Li M. A narrative review of machine learning as promising revolution in clinical practice of scoliosis. Ann Transl Med [Internet]. 2021 [cited 2021 Mar 3];9(1):67. https://doi.org/10.21037/atm-20-5495.

  63. Hassen YAM, Johnston MJ, Singh P, Pucher PH, Darzi A. Key components of the safe surgical ward. Ann Surg [Internet]. 2019 Jun 1 [cited 2021 Mar 2];269(6):1064–72. https://journals.lww.com/00000658-201906000-00011

  64. NHS England. Factsheet: implementation of the “Sepsis Six” care bundle. 2014;(February):2013–5. https://www.england.nhs.uk/wp-content/uploads/2014/02/rm-fs-10-1.pdf

  65. Helling TS, Martin LC, Martin M, Mitchell ME. Failure events in transition of care for surgical patients. J Am Coll Surg. 2014;218(4):723–31.

    PubMed  Google Scholar 

  66. Sun EC, Darnall BD, Baker LC, MacKey S. Incidence of and risk factors for chronic opioid use among opioid-naive patients in the postoperative period. JAMA Intern Med [Internet]. 2016 Sep 1 [cited 2021 Mar 3];176(9):1286–93. https://jamanetwork.com/

  67. Loftus TJ, Tighe PJ, Filiberto AC, Balch J, Upchurch GR, Rashidi P, et al. Opportunities for machine learning to improve surgical ward safety. Am J Surg [Internet]. 2020;220(4):905–13. https://doi.org/10.1016/j.amjsurg.2020.02.037.

    Article  Google Scholar 

  68. Barker FG. Efficacy of prophylactic antibiotic therapy in spinal surgery: a meta-analysis. Neurosurgery [Internet]. 2002 Aug 1 [cited 2021 Mar 3];51(2):391–401. https://academic.oup.com/neurosurgery/article/2739794/Efficacy

  69. Hopkins BS, Mazmudar A, Driscoll C, Svet M, Goergen J, Kelsten M, et al. Using artificial intelligence (AI) to predict postoperative surgical site infection: a retrospective cohort of 4046 posterior spinal fusions. Clin Neurol Neurosurg [Internet]. 2020;192(December 2019):105718. https://doi.org/10.1016/j.clineuro.2020.105718.

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Mehdian, R., Howard, M. (2022). Artificial Intelligence in Trauma and Orthopedics. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_256

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64573-1_256

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64572-4

  • Online ISBN: 978-3-030-64573-1

  • eBook Packages: MedicineReference Module Medicine

Publish with us

Policies and ethics