The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.
Ardern CL, Ekås GR, Grindem H, Moksnes H, Anderson AF, Chotel F, Cohen M, Forssblad M, Ganley TJ, Feller JA, Karlsson J, Kocher MS, LaPrade RF, McNamee M, Mandelbaum B, Micheli L, Mohtadi N, Reider B, Roe J, Seil R, Siebold R, Silvers-Granelli HJ, Soligard T, Witvrouw E, Engebretsen L (2018) 2018 International Olympic Committee consensus statement on prevention, diagnosis and management of paediatric anterior cruciate ligament (ACL) injuries. Br J Sports Med 52:422–438 DOI: 10.1136/bjsports-2018-099060
Berwick DM (2003) Disseminating innovations in health care. JAMA 289:1969–1975 DOI: 10.1001/jama.289.15.1969
Cabitza F, Locoro A, Banfi G (2018) Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol 6:75 DOI: 10.3389/fbioe.2018.00075
Choi JW, Cho YJ, Lee S, Lee J, Lee S, Choi YH, Cheon J-E, Ha JY (2020) Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Investig Radiol 55:101–110 DOI: 10.1097/RLI.0000000000000615
Collins GS, Reitsma JB, Altman DG, Moons KGM (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162:55–63 DOI: 10.7326/M14-0697
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118 DOI: 10.1038/nature21056
Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ (2021) Machine learning algorithms predict functional improvement after hip arthroscopy for femoroacetabular impingement syndrome in athletes. J Bone Jt Surg 103:1055–1062 DOI: 10.2106/JBJS.20.01640
Liu Y, Chen P-HC, Krause J, Peng L (2019) How to read articles that use machine learning: users’ guides to the medical literature. JAMA 322:1806–1816 DOI: 10.1001/jama.2019.16489
Lu Y, Forlenza E, Cohn MR, Lavoie-Gagne O, Wilbur RR, Song BM, Krych AJ, Forsythe B (2020) Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc. 10.1007/s00167-020-06321-w DOI: 10.1007/s00167-020-06321-w
Lu Y, Forlenza E, Wilbur RR, Lavoie-Gagne O, Fu MC, Yanke AB, Cole BJ, Verma N, Forsythe B (2021) Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surg Sports Traumatol Arthrosc. 10.1007/s00167-020-06421-7 DOI: 10.1007/s00167-020-06421-7
Pareek A, Parkes CW, Bernard CD, Abdel MP, Saris DBF, Krych AJ (2020) The SIFK score: a validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee. Knee Surg Sports Traumatol Arthrosc 28:3149–3155 DOI: 10.1007/s00167-019-05792-w
Ramkumar PN, Karnuta JM, Haeberle HS, Owusu-Akyaw KA, Warner TS, Rodeo SA, Nwachukwu BU, Williams RJ (2021) Association between preoperative mental health and clinically meaningful outcomes after osteochondral allograft for cartilage defects of the knee: a machine learning analysis. Am J Sports Med 49:948–957 DOI: 10.1177/0363546520988021
Ramkumar PN, Karnuta JM, Haeberle HS, Rodeo SA, Nwachukwu BU, Williams RJ (2021) Effect of preoperative imaging and patient factors on clinically meaningful outcomes and quality of life after osteochondral allograft transplantation: a machine learning analysis of cartilage defects of the knee. Am J Sports Med 49:2177–2186 DOI: 10.1177/03635465211015179
Rouzrokh P, Wyles CC, Philbrick KA, Ramazanian T, Weston AD, Cai JC, Taunton MJ, Lewallen DG, Berry DJ, Erickson BJ, Maradit Kremers H (2021) A deep learning tool for automated radiographic measurement of acetabular component inclination and version after total hip arthroplasty. J Arthroplasty 36(7):2510–2517.e6. 10.1016/j.arth.2021.02.026 DOI: 10.1016/j.arth.2021.02.026
Schock J, Truhn D, Abrar DB, Merhof D, Conrad S, Post M, Mittelstrass F, Kuhl C, Nebelung S (2021) Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol Artif Intell 3:e200198 DOI: 10.1148/ryai.2020200198
Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710 DOI: 10.1038/s41586-019-1923-7
Subramanian P, Kantharuban S, Subramanian V, Willis-Owen SAG, Willis-Owen CA (2011) Orthopaedic surgeons: as strong as an ox and almost twice as clever? Multicentre prospective comparative study. BMJ 343:d7506–d7506 DOI: 10.1136/bmj.d7506
Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N (2019) Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skelet Radiol 48:239–244 DOI: 10.1007/s00256-018-3016-3
Van Eetvelde H, Mendonça LD, Ley C, Seil R, Tischer T (2021) Machine learning methods in sport injury prediction and prevention: a systematic review. J Exp Orthop 8:27 DOI: 10.1186/s40634-021-00346-x
Yamada Y, Maki S, Kishida S, Nagai H, Arima J, Yamakawa N, Iijima Y, Shiko Y, Kawasaki Y, Kotani T, Shiga Y, Inage K, Orita S, Eguchi Y, Takahashi H, Yamashita T, Minami S, Ohtori S (2020) Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. Acta Orthop 91:699–704 DOI: 10.1080/17453674.2020.1803664
