Skip to main content

Advertisement

SpringerLink
Log in

A Surgeon’s Guide to Understanding Artificial Intelligence and Machine Learning Studies in Orthopaedic Surgery

  • Updates in Spine Surgery - Techniques, Biologics, and Non-Operative Management (W Hsu, Section Editor)
  • Published:
Current Reviews in Musculoskeletal Medicine Aims and scope Submit manuscript

Abstract

Purpose of Review

In recent years, machine learning techniques have been increasingly utilized across medicine, impacting the practice and delivery of healthcare. The data-driven nature of orthopaedic surgery presents many targets for improvement through the use of artificial intelligence, which is reflected in the increasing number of publications in the medical literature. However, the unique methodologies utilized in AI studies can present a barrier to its widespread acceptance and use in orthopaedics. The purpose of our review is to provide a tool that can be used by practitioners to better understand and ultimately leverage AI studies.

Recent Findings

The increasing interest in machine learning across medicine is reflected in a greater utilization of AI in recent medical literature. The process of designing machine learning studies includes study design, model choice, data collection/handling, model development, training, testing, and interpretation. Recent studies leveraging ML in orthopaedics provide useful examples for future research endeavors.

Summary

This manuscript intends to create a guide discussing the use of machine learning and artificial intelligence in orthopaedic surgery research. Our review outlines the process of creating a machine learning algorithm and discusses the different model types, utilizing examples from recent orthopaedic literature to illustrate the techniques involved.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

N/A

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. 2020;25-60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

  2. Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. J Bone Joint Surg Am. 2020;102(9):830–40. https://doi.org/10.2106/JBJS.19.01128.

    Article  PubMed  Google Scholar 

  3. Maffulli N, Rodriguez HC, Stone IW, et al. Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol. J Orthop Surg Res. 2020;15(1):478. https://doi.org/10.1186/s13018-020-02002-z Published 2020 Oct 19.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Makhni EC, Makhni S, Ramkumar PN. Artificial Intelligence for the Orthopaedic Surgeon: An Overview of Potential Benefits, Limitations, and Clinical Applications. J Am Acad Orthop Surg. 2021;29(6):235–43. https://doi.org/10.5435/JAAOS-D-20-00846.

    Article  PubMed  Google Scholar 

  5. Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE, Spitzer AI, Ramkumar PN. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr Rev Musculoskelet Med. 2020;13(1):69–76. https://doi.org/10.1007/s12178-020-09600-8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep. 2019;49(1):15–21. https://doi.org/10.1002/hast.973Perspective piece discussing the use of artificial intelligence in medicine that argues against prioritizing explainability or interpretability over predictive and diagnostic accuracy when our knowledge of causal associations is lacking.

    Article  PubMed  Google Scholar 

  7. Kluzek S, Mattei TA. Machine-learning for osteoarthritis research. Osteoarthritis Cartilage. 2019;27(7):977–8. https://doi.org/10.1016/j.joca.2019.04.005.

    Article  CAS  PubMed  Google Scholar 

  8. Kalmet PHS, Sanduleanu S, Primakov S, et al. Deep learning in fracture detection: a narrative review. Acta Orthop. 2020;91(2):215–20. https://doi.org/10.1080/17453674.2019.1711323.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ramkumar PN, Haeberle HS, Bloomfield MR, Schaffer JL, Kamath AF, Patterson BM, Krebs VE. Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring. J Arthroplasty. 2019;34(10):2204–9. https://doi.org/10.1016/j.arth.2019.06.018.

    Article  PubMed  Google Scholar 

  10. Hendriksen JM, Geersing GJ, Moons KG, de Groot JA. Diagnostic and prognostic prediction models. J Thromb Haemost. 2013;11(Suppl 1):129–41. https://doi.org/10.1111/jth.12262.

    Article  PubMed  Google Scholar 

  11. Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19(1):281. https://doi.org/10.1186/s12911-019-1004-8 Published 2019 Dec 21.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. https://doi.org/10.1186/s12874-019-0681-4 Published 2019 Mar 19.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mehta SD, Sebro R. 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. 2020;33(1):204–10. https://doi.org/10.1007/s10278-019-00224-0.

    Article  PubMed  Google Scholar 

  14. Venäläinen MS, Panula VJ, Klén R, et al. Preoperative Risk Prediction Models for Short-Term Revision and Death After Total Hip Arthroplasty: Data from the Finnish Arthroplasty Register. JB JS Open Access. 2021;6(1):e20.00091. https://doi.org/10.2106/JBJS.OA.20.00091. Published 2021 Jan 25 Utilized data from 25,919 total hip arthroplasties to create a lasso regression determining the likelihood of patients experiencing common risk factors of treatment failure.

  15. Zhao B, Waterman RS, Urman RD, Gabriel RA. A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery. J Med Syst. 2019;43(2):32. https://doi.org/10.1007/s10916-018-1151-y. Published 2019 Jan 5 Employed ridge regression to predict durations of robot-assisted surgeries.

  16. Baca Q, Marti F, Poblete B, Gaudilliere B, Aghaeepour N, Angst MS. Predicting Acute Pain After Surgery: A Multivariate Analysis. Ann Surg. 2021;273(2):289–98. https://doi.org/10.1097/SLA.0000000000003400.

    Article  PubMed  Google Scholar 

  17. Zhong H, Poeran J, Gu A, Wilson LA, Gonzalez Della Valle A, Memtsoudis SG, Liu J. Machine learning approaches in predicting ambulatory same day discharge patients after total hip arthroplasty. Reg Anesth Pain Med. 2021;46(9):779–83. https://doi.org/10.1136/rapm-2021-102715.

    Article  PubMed  Google Scholar 

  18. Dolatabadi E, Taati B, Mihailidis A. Automated classification of pathological gait after stroke using ubiquitous sensing technology. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:6150–3. https://doi.org/10.1109/EMBC.2016.7592132.

    Article  PubMed  Google Scholar 

  19. Nick TG, Campbell KM. Logistic regression. Methods Mol Biol. 2007;404:273–301. https://doi.org/10.1007/978-1-59745-530-5_14.

    Article  CAS  PubMed  Google Scholar 

  20. Schneider A, Hommel G, Blettner M. Linear regression analysis: part 14 of a series on evaluation of scientific publications. Dtsch Arztebl Int. 2010;107(44):776–82. https://doi.org/10.3238/arztebl.2010.0776.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ben-Hur A, Weston J. A user’s guide to support vector machines. Methods Mol Biol. 2010;609:223–39. https://doi.org/10.1007/978-1-60327-241-4_13.

    Article  CAS  PubMed  Google Scholar 

  22. Long RG. The crux of the method: assumptions in ordinary least squares and logistic regression. Psychol Rep. 2008;103(2):431–4. https://doi.org/10.2466/pr0.103.2.431-434.

    Article  PubMed  Google Scholar 

  23. Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB. Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. 2014;14:116. https://doi.org/10.1186/1471-2288-14-116 Published 2014 Oct 16.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Arashi M, Roozbeh M, Hamzah NA, Gasparini M. Ridge regression and its applications in genetic studies. PLoS One. 2021;16(4):e0245376. https://doi.org/10.1371/journal.pone.0245376 Published 2021 Apr 8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Waldmann P, Mészáros G, Gredler B, Fuerst C, Sölkner J. Evaluation of the lasso and the elastic net in genome-wide association studies published correction appears in Front Genet. 2014;5:349.. Front Genet. 2013;4:270. https://doi.org/10.3389/fgene.2013.00270 Published 2013 Dec 4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Song YY, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry. 2015;27(2):130–5. https://doi.org/10.11919/j.issn.1002-0829.215044.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947–58. https://doi.org/10.1021/ci034160g.

    Article  CAS  PubMed  Google Scholar 

  28. Merali ZG, Witiw CD, Badhiwala JH, Wilson JR, Fehlings MG. Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PLoS One. 2019;14(4):e0215133. https://doi.org/10.1371/journal.pone.0215133. Published 2019 Apr 4 Created several ML models to predict postoperative outcomes in patients with degenerative cervical myelopathy, with the random forest being most accurate.

  29. Zhang Z. Introduction to machine learning: k-nearest neighbors. Ann Transl Med. 2016;4(11):218. https://doi.org/10.21037/atm.2016.03.37.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhang Z. A gentle introduction to artificial neural networks. Ann Transl Med. 2016;4(19):370. https://doi.org/10.21037/atm.2016.06.20.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29. https://doi.org/10.1007/s13244-018-0639-9.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lindsey R, Daluiski A, Chopra S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018;115(45):11591–6. https://doi.org/10.1073/pnas.1806905115Created a neural network model to assist clinicians in detecting fractures, utilizing a sample of 135,409 radiographs annotated by 18 senior orthopaedic surgeons.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rodríguez-Pérez R, Bajorath J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J Comput Aided Mol Des. 2020;34(10):1013–26. https://doi.org/10.1007/s10822-020-00314-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dobbin KK, Simon RM. Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics. 2011;4:31. https://doi.org/10.1186/1755-8794-4-31 Published 2011 Apr 8.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Nichols JA, Herbert Chan HW, Baker MAB. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev. 2019;11(1):111–8. https://doi.org/10.1007/s12551-018-0449-9.

    Article  PubMed  Google Scholar 

  36. Linardatos P, Papastefanopoulos V. Kotsiantis S. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy (Basel). 2020;23(1):18. https://doi.org/10.3390/e23010018 Published 2020 Dec 25.

    Article  Google Scholar 

  37. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18(5):544–51. https://doi.org/10.1136/amiajnl-2011-000464.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Code Availability

N/A

Author information

Authors and Affiliations

  1. Northwestern University, Evanston, IL, USA

    Rohan M Shah

  2. Department of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Clarissa Wong, Nicholas C Arpey, Alpesh A Patel & Srikanth N Divi

Authors
  1. Rohan M Shah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Clarissa Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas C Arpey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alpesh A Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Srikanth N Divi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srikanth N Divi.

Ethics declarations

Conflict of Interest

The authors do not have any relevant conflicts of interest, sources of financial support, corporate involvement, or patent holdings to disclose.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Updates in Spine Surgery - Techniques, Biologics, and Non-Operative Management

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shah, R.M., Wong, C., Arpey, N.C. et al. A Surgeon’s Guide to Understanding Artificial Intelligence and Machine Learning Studies in Orthopaedic Surgery. Curr Rev Musculoskelet Med 15, 121–132 (2022). https://doi.org/10.1007/s12178-022-09738-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12178-022-09738-7

Keywords

Search

Navigation