Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics
Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD–CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.
KeywordsArtificial intelligence Orthopaedic surgery Machine learning
Convolutional neural network
Artificial neural network
Recurrent neural network
Concepts: MP, AG, SM, VB, and AS. Design: MP, AG, SM, VB, and AS. Definition of intellectual content: MP, AG, SM, VB, and AS. Literature search: MP, AG, SM, VB, and AS. Clinical studies: MP, AG, SM, VB, and AS. Experimental studies: not available. Data acquisition: none. Data analysis: none. Statistical analysis: not available. Manuscript preparation: MP, AG, SM, VB, and AS. Manuscript editing: MP, AG, SM, VB, and AS. Manuscript review: MP, AG, SM, VB, and AS. Guarantor: MP, AG, SM, VB, and AS.
Compliance with Ethical Standards
Conflict of interest
The authors declare no conflict of interests in relation to the content published here which is entirely educative and informative.
- 1.Nadella, S. (2016). The partnership of the future, SLATE: June 28 2016. https://slate.com/technology/2016/06/microsoft-ceo-satya-nadella-humans-and-a-i-can-work-together-to-solve-societys-challenges.html. Accessed: 19 Jun 2019.
- 2.Turing, A. M. (2019). Computing Machinery and Intelligence, Mind, New Series, Vol. 59, No. 236 (Oct., 1950), pp. 433–460 Published by: Oxford University Press on behalf of the Mind Association available at: http://www.jstor.org/stable/2251299. Accessed: 19 Jun 2019.
- 3.Domingo, P. (2017). The machine learning revolution. In The master algorithm: How the quest for the ultimate learning machine will remake our world (pp. 1–22). UK: Penguin Random House.Google Scholar
- 4.Woodson, J. (2019). Decades Ago, Pilots Learned to “Fly by Instruments.” Doctors Need to Do the Same [Internet]. Harvard Business Review. 2019 [cited 23 June 2019]. https://hbr.org/2018/03/decades-ago-pilots-learned-to-fly-by-instruments-doctors-need-to-do-the-same.
- 5.McCarthy, J., Marvin, L., Minsky, M. L., Rochester, N., & Shannon, C. E. (1995). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. August 31, 1955. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. Accessed June 23 2019.
- 6.Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., & Hager, G., et al. (2019). Artificial Intelligence and Life in 2030.”One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA, September 2016. http://ai100.stanford.edu/2016-report. Accessed June 23 2019.
- 7.Hintz A. (2019). Understanding the four types of AI, from reactive robots to self-aware beings [Internet]. The Conversation. 2019 [cited 28 July 2019]. Available from: https://theconversation.com/understanding-the-four-types-of-ai-from-reactive-robots-to-self-aware-beings-67616. Accessed 28 July 2018.
- 8.Cool vendors in healthcare artificial intelligence. https://www.gartner.com/document/3913322 Accessed June 23 2017
- 10.McCarthy, J. (2019). What is AI? http://jmc.stanford.edu/articles/whatisai/whatisai.pdf. Accessed 23 June 2019.
- 11.Russel, S. J., & Norvig, P. (2015). Introduction. In Artificial intelligence: A modern approach (3rd ed., pp. 1–3). New Delhi: Pearson India Education Services Pvt Ltd.Google Scholar
- 13.Russel, S. J., & Norvig, P. (2015). Learning from examples. Artificial intelligence: A modern approach (3rd ed., pp. 706–781). New Delhi: Pearson India Education Services Pvt Ltd.Google Scholar
- 16.Colton, S., & Mentor, F. R. C. (2007). “The balance filter.” Presentation, Massachusetts Institute of Technology (2007). http://d1.amobbs.com/bbs_upload782111/files_44/ourdev_665531S2JZG6.pdf. Accessed 14 Dec 2019.
- 19.Pai-shun, T., Chun-Chen, T., Pin-Yu, C., Ya-Yun, L., & Shin-Ming, C. (2019). FEAST: An automated feature selection framework for compilation tasks. [Internet]. Arxiv.org. 2016 [cited 28 July 2019]. Available from: https://arxiv.org/pdf/1610.09543.pdf.
- 23.‘Software as a medical device (SaMD)”. (2019). https://www.fda.gov/medical-devices/digital-health/software-medical-device-samd. Accessed 28 June 2019.
- 24.“Artificial Intelligence and machine learning in SaMD”. (2019). https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed 28 June 2019.
- 25.Developing Software Pre-certification program: A Working Model” . (2019). https://www.fda.gov/media/113802/download. Accessed 28 June 2019.
- 27.Keikes, L., Medlock, S., van de Berg, D. J., Zhang, S., Guicherit, O. R., Punt, C. J. A., et al. (2018). The first steps in the evaluation of a “black-box” decision support tool: a protocol and feasibility study for the evaluation of Watson for Oncology. Journal Of Clinical and Translational Research,3(Suppl 3), 411–423.PubMedPubMedCentralGoogle Scholar
- 39.Choi, Y. I., Chung, J. W., Kim, K. O., Kwon, K. A., Kim, Y. J., Park, D. K., et al. (2019). Concordance rate between clinicians and watson for oncology among patients with advanced gastric cancer: Early, real-world experience in Korea. Canadian Journal of Gastroenterology and Hepatology,2019, 8072928.PubMedCrossRefPubMedCentralGoogle Scholar
- 43.Kim, J. S., Merrill, R. K., Arvind, V., Kaji, D., Pasik, S. D., Nwachukwu, C. C., et al. (2018). Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine (Phila Pa 1976),43(12), 853–860.CrossRefGoogle Scholar
- 45.Jamaludin, A., Lootus, M., Kadir, T., Zisserman, A., Urban, J., Battié, M. C., et al. (2017). ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. European Spine Journal,26(5), 1374–1383.PubMedCrossRefGoogle Scholar
- 46.Oh, E., Seo, S. W., Yoon, Y. C., Kim, D. W., Kwon, S., & Yoon, S. (2017). Prediction of pathologic femoral fractures in patients with lung cancer using machine learning algorithms: Comparison of computed tomography-based radiological features with clinical features versus without clinical features. Journal of Orthopaedic Surgery (Hong Kong),25(2), 2309499017716243.Google Scholar
- 47.Janssen, S. J., van der Heijden, A. S., van Dijke, M., Ready, J. E., Raskin, K. A., Ferrone, M. L., et al. (2015). 2015 Marshall urist young investigator award: Prognostication in patients with long bone metastases: Does a boosting algorithm improve survival estimates? Clinical Orthopaedics and Related Research,473(10), 3112–3121.PubMedPubMedCentralCrossRefGoogle Scholar
- 48.Thio, Q. C. B. S., Karhade, A. V., Ogink, P. T., Raskin, K. A., De Amorim, Bernstein K., Lozano Calderon, S. A., et al. (2018). Can machine-learning techniques be used for 5-year survival prediction of patients with chondrosarcoma? Clinical Orthopaedics and Related Research,476(10), 2040–2048.PubMedPubMedCentralCrossRefGoogle Scholar
- 49.Bongers, M. E. R., Thio, Q. C. B. S., Karhade, A. V., Storm, M. L., Raskin, K. A., Lozano Calderon, S. A., et al. (2019). Does the SORG Algorithm predict 5-year survival in patients with chondrosarcoma? An external validation. Clinical Orthopaedics and Related Research, 477, 2296–2303.CrossRefGoogle Scholar
- 50.Piccioli, A., Spinelli, M. S., Forsberg, J. A., Wedin, R., Healey, J. H., Ippolito, V., et al. (2015). How do we estimate survival? External validation of a tool for survival estimation in patients with metastatic bone disease-decision analysis and comparison of three international patient populations. BMC Cancer,22(15), 424.CrossRefGoogle Scholar
- 51.Ogura, K., Gokita, T., Shinoda, Y., Kawano, H., Takagi, T., Ae, K., et al. (2017). Can a multivariate model for survival estimation in skeletal metastases (PATHFx) be externally validated using japanese patients? Clinical Orthopaedics and Related Research,475(9), 2263–2270.PubMedPubMedCentralCrossRefGoogle Scholar
- 55.Ashinsky, B. G., Bouhrara, M., Coletta, C. E., Lehallier, B., Urish, K. L., Lin, P. C., et al. (2017). Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative. Journal of Orthopaedic Research,35(10), 2243–2250.PubMedPubMedCentralCrossRefGoogle Scholar
- 56.Schmaranzer, F., Helfenstein, R., Zeng, G., Lerch, T. D., Novais, E. N., Wylie, J. D., et al. (2019). Automatic MRI-based three-dimensional models of hip cartilage provide improved morphologic and biochemical analysis. Clinical Orthopaedics and Related Research,477(5), 1036–1052.PubMedCrossRefGoogle Scholar
- 59.Fontana, M. A., Lyman, S., Sarker, G. K., Padgett, D. E., & MacLean, C. H. (2019). Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clinical Orthopaedics and Related Research,477(6), 1267–1279.PubMedCrossRefGoogle Scholar
- 60.Harris, A. H. S., Kuo, A. C., Weng, Y., Trickey, A. W., Bowe, T., & Giori, N. J. (2019). Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty? Clinical Orthopaedics and Related Research,477(2), 452–460.PubMedCrossRefPubMedCentralGoogle Scholar
- 61.Harris, A. H., Kuo, A. C., Bowe, T., Gupta, S., Nordin, D., & Giori, N. J. (2018). Prediction models for 30-day mortality and complications after total knee and hip arthroplasties for veteran health administration patients with osteoarthritis. Journal of Arthroplasty,33(5), 1539–1545.PubMedCrossRefPubMedCentralGoogle Scholar
- 62.Ramkumar, P. N., Navarro, S. M., Haeberle, H. S., Karnuta, J. M., Mont, M. A., Iannotti, J. P., et al. (2019). Development and validation of a machine learning algorithm after primary total hip arthroplasty: Applications to length of stay and payment models. Journal of Arthroplasty,34(4), 632–637.PubMedCrossRefPubMedCentralGoogle Scholar
- 63.Navarro, S. M., Wang, E. Y., Haeberle, H. S., Mont, M. A., Krebs, V. E., Patterson, B. M., et al. (2018). Machine learning and primary total knee arthroplasty: Patient forecasting for a patient-specific payment model. Journal of Arthroplasty,33(12), 3617–3623.PubMedCrossRefPubMedCentralGoogle Scholar
- 64.Ramkumar, P. N., Haeberle, H. S., Bloomfield, M. R., Schaffer, J. L., Kamath, A. F., Patterson, B. M., et al. (2019). 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. The Journal of Arthroplasty. https://doi.org/10.1016/j.arth.2019.06.018.CrossRefPubMedGoogle Scholar
- 72.Trends emerge in the Gartner Hype cycle for emerging technologies. (2018). https://www.gartner.com/smarterwithgartner/5-trends-emerge-in-gartner-hype-cycle-for-emerging-technologies-2018/. Accessed 21 July 2018.