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Artificial Intelligence and Healthcare Decision-Making

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Sports Injuries

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Abstract

Artificial intelligence, and more specifically, its subset machine learning, have been increasingly impacting the field of orthopedic surgery. The number of machine learning-related publications within the orthopedic literature has been increasing every year. Models can primarily be used to help clinicians with both diagnostic and prognostic tasks and will likely play a major role in clinical practice in the near future. Despite the many obstacles to overcome in order to achieve the ultimate goal of improved patient care through AI-powered advancements, the future is promising and clinicians should familiarize themselves with the basics of clinical artificial intelligence. Unfortunately, at this time, there has been little widespread adoption of machine learning algorithms into daily practice and many orthopedic surgeons remain cautious regarding these novel statistical techniques. The purpose of this chapter is to introduce artificial intelligence as it relates to orthopedic surgery and sports medicine, providing examples from the literature along the way.

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References

  • Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ et al (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394(10201):861–867

    Article  PubMed  Google Scholar 

  • Ayala F, López-Valenciano A, Gámez Martín JA, De Ste CM, Vera-Garcia FJ, García-Vaquero MDP et al (2019) A preventive model for hamstring injuries in professional soccer: learning algorithms. Int J Sports Med 40(5):344–353

    Article  PubMed  PubMed Central  Google Scholar 

  • Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E et al (2018) Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med 15(11):e1002699-e

    Article  Google Scholar 

  • Brund RBK, Waagepetersen R, O. Nielsen R, Rasmussen J, Nielsen MS, Andersen CH, de Zee M (2021) How precisely can easily accessible variables predict Achilles and Patellar tendon forces during running? Sensors 21(21):7418

    Article  PubMed  PubMed Central  Google Scholar 

  • Burns JE, Yao J, Summers RM (2017) Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology 284(3):788–797

    Article  PubMed  Google Scholar 

  • Cheng CT, Ho TY, Lee TY, Chang CC, Chou CC, Chen CC, Chung IF, Liao CH (2019) Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol 29(10):5469–5477

    Article  PubMed  PubMed Central  Google Scholar 

  • Choi JW, Cho YJ, Lee S, Lee J, Lee S, Choi YH, Cheon JE, Ha JY (2020) Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Investig Radiol 55(2):101–110

    Article  Google Scholar 

  • Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl KD (2020) MI in healthcare workshop working group. Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency. NPJ Digit Med 26(3):47

    Article  Google Scholar 

  • De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S et al (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24(9):1342–1350

    Article  PubMed  Google Scholar 

  • 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(7639):115–118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hardenberg M, Speklé EM, Coenen P, Brus IM, Kuijer P (2022) The economic burden of knee and hip osteoarthritis: absenteeism and costs in the Dutch workforce. BMC Musculoskelet Disord 23(1):364

    Article  PubMed  PubMed Central  Google Scholar 

  • Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G (2020) Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 132(2):379–394

    Article  PubMed  Google Scholar 

  • Hill BG, Krogue JD, Jevsevar DS, Schilling PL (2022) Deep learning and imaging for the orthopaedic surgeon: how machines “read” radiographs. J Bone Joint Surg Am 104(18):1675–1686

    Article  PubMed  Google Scholar 

  • Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S, Lenane P, Moloney FJ, Yazdabadi A (2020) Artificial intelligence in dermatology-where we are and the way to the future: a review. Am J Clin Dermatol 21(1):41–47

    Article  PubMed  Google Scholar 

  • Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500–510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Karhade AV, Schwab JH, Bedair H (2019) Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplast 34(10):2272–2277.e1

    Article  Google Scholar 

  • Karnuta JM, Luu BC, Haeberle HS, Saluan PM, Frangiamore SJ, Stearns KL et al (2020) Machine learning outperforms regression analysis to predict next-season major league baseball player injuries: epidemiology and validation of 13,982 player-years from performance and injury profile trends, 2000–2017. Orthop J Sports Med 8(11):2325967120963046

    Article  PubMed  PubMed Central  Google Scholar 

  • Khan MA, Kadry S, Parwekar P, Damaševičius R, Mehmood A, Khan JA et al (2021) Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine. Complex Intell Syst 9:2665–2683

    Article  Google Scholar 

  • Kotti M, Duffell LD, Faisal AA, McGregor AH (2017) Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys 43:19–29

    Article  PubMed  PubMed Central  Google Scholar 

  • Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, Zaid M, McGill KC, Patel R, Sohn JH, Wright A, Darger BF, Padrez KA, Ozhinsky E, Majumdar S, Pedoia V (2020) Automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell 2(2):e190023

    Article  PubMed  PubMed Central  Google Scholar 

  • Kunze KN, Krivicich LM, Clapp IM, Bodendorfer BM, Nwachukwu BU, Chahla J, Nho SJ (2021a) Machine learning algorithms predict achievement of clinically significant outcomes after orthopaedic surgery: a systematic review. Arthroscopy 38(6):2090–2105

    Article  PubMed  Google Scholar 

  • Kunze KN, Polce EM, Ranawat AS, Randsborg PH, Williams RJ 3rd, Allen AA et al (2021b) Application of machine learning algorithms to predict clinically meaningful improvement after arthroscopic anterior cruciate ligament reconstruction. Orthop J Sports Med 9(10):23259671211046575

    Article  PubMed  PubMed Central  Google Scholar 

  • Kunze KN, Polce EM, Clapp IM, Alter T, Nho SJ (2022) Association between preoperative patient factors and clinically meaningful outcomes after hip arthroscopy for femoroacetabular impingement syndrome: a machine learning analysis. Am J Sports Med 50(3):746–756

    Article  PubMed  Google Scholar 

  • Labbé DR, Li D, Grimard G, de Guise JA, Hagemeister N (2015) Quantitative pivot shift assessment using combined inertial and magnetic sensing. Knee Surg Sports Traumatol Arthrosc 23(8):2330–2338

    Article  PubMed  Google Scholar 

  • Lattanzi E, Donati M, Freschi V (2022) Exploring artificial neural networks efficiency in tiny wearable devices for human activity recognition. Sensors (Basel) 22(7):263750

    Article  Google Scholar 

  • Liaqat S, Dashtipour K, Rizwan A, Usman M, Shah SA, Arshad K, Assaleh K, Ramzan N (2022) Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing. Sci Rep 12(1):3715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu F, Guan B, Zhou Z, Samsonov A, Rosas H, Lian K, Sharma R, Kanarek A, Kim J, Guermazi A, Kijowski R (2019) Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol Artif Intell 1(3):180091

    Article  PubMed  PubMed Central  Google Scholar 

  • Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM et al (2020) Artificial intelligence and surgical decision-making. JAMA Surg 155(2):148–158

    Article  PubMed  PubMed Central  Google Scholar 

  • Lollixzc (2022) Machine learning as a subset of artificial intelligence. https://commons.wikimedia.org/wiki/File:AI_hierarchy.svg

  • Martin RK, Pareek A, Krych AJ, Maradit Kremers H, Engebretsen L (2021) Machine learning in sports medicine: need for improvement. J ISAKOS 6(1):1–2

    Article  PubMed  Google Scholar 

  • Martin RK, Wastvedt S, Pareek A, Persson A, Visnes H, Fenstad AM et al (2022) Predicting anterior cruciate ligament reconstruction revision: a machine learning analysis utilizing the Norwegian knee ligament register. J Bone Joint Surg Am 104(2):145–153

    Article  PubMed  Google Scholar 

  • Nwachukwu BU, Beck EC, Lee EK, Cancienne JM, Waterman BR, Paul K et al (2020) Application of machine learning for predicting clinically meaningful outcome after arthroscopic femoroacetabular impingement surgery. Am J Sports Med 48(2):415–423

    Article  PubMed  Google Scholar 

  • Ozaydin B, Berner ES, Cimino JJ (2021) Appropriate use of machine learning in healthcare. Intell Based Med 5:100041

    Article  Google Scholar 

  • Parkkari J, Kujala UM, Kannus P (2001) Is it possible to prevent sports injuries? Review of controlled clinical trials and recommendations for future work. Sports Med 31(14):985–995

    Article  CAS  PubMed  Google Scholar 

  • Paschos NK (2021) Editorial commentary: artificial intelligence in sports medicine diagnosis needs to improve. Arthroscopy 37(2):782–783

    Article  PubMed  Google Scholar 

  • Perera NS, Joel J, Bunola JA (2013) Anterior cruciate ligament rupture: delay to diagnosis. Injury 44(12):1862–1865

    Article  CAS  PubMed  Google Scholar 

  • Pugliese R, Regondi S, Marini R (2021) Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Sci Manag 4:19–29

    Article  Google Scholar 

  • Ramkumar PN, Haeberle HS, Bloomfield MR, Schaffer JL, Kamath AF, Patterson BM, Krebs VE (2019a) 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 Arthroplast 34(10):2204–2209

    Article  Google Scholar 

  • Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA, Bloomfield M, Patterson BM (2019b) Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplast 34(10):2253–2259

    Article  Google Scholar 

  • Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE (2019c) Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models. J Arthroplast 34(4):632–637

    Article  Google Scholar 

  • Rommers N, Rössler R, Verhagen E, Vandecasteele F, Verstockt S, Vaeyens R, Lenoir M, D’Hondt E, Witvrouw E (2020) A machine learning approach to assess injury risk in elite youth football players. Med Sci Sports Exerc 52(8):1745–1751

    Article  PubMed  Google Scholar 

  • Scheid BH, Aradi S, Pierson RM, Baldassano S, Tivon I, Litt B, Gonzalez-Alegre P (2022) Predicting severity of Huntington’s disease with wearable sensors. Front Digit Health 4:874208

    Article  PubMed  PubMed Central  Google Scholar 

  • Shah RF, Bini SA, Martinez AM, Pedoia V, Vail TP (2020a) Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms. Bone Joint J 102(6 Supple A):101–106

    Article  PubMed  Google Scholar 

  • Shah RF, Bini S, Vail T (2020b) Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients. Bone Joint J 102-b(7_Supple_B):99–104

    Article  PubMed  Google Scholar 

  • Shei RJ, Holder IG, Oumsang AS, Paris BA, Paris HL (2022) Wearable activity trackers-advanced technology or advanced marketing? Eur J Appl Physiol 122(9):1975–1990

    Article  PubMed  PubMed Central  Google Scholar 

  • Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R et al (2019) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103(2):167–175

    Article  PubMed  Google Scholar 

  • Toonders J (2018) Data is the new oil of the digital economy. Available from: https://www.wired.com/insights/2014/07/data-new-oil-digital-economy/#:~:text=Data%20in%20the%2021st%20Century,is%20more%20valuable%20than%20ever

  • Trasolini NA, Nicholson KF, Mylott J, Bullock GS, Hulburt TC, Waterman BR (2022) Biomechanical analysis of the throwing athlete and its impact on return to sport. Arthrosc Sports Med Rehabil 4(1):e83–e91

    Article  PubMed  PubMed Central  Google Scholar 

  • Urish K, Reznik AM (2018) How would a computer diagnose arthritis on a radiograph? AAOS Now December:32–33

    Google Scholar 

  • Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025 (2023). Available from: https://www.statista.com/statistics/871513/worldwide-data-created/

  • Whiteside D, Martini DN, Lepley AS, Zernicke RF, Goulet GC (2016) Predictors of ulnar collateral ligament reconstruction in major league baseball pitchers. Am J Sports Med 44(9):2202–2209

    Article  PubMed  Google Scholar 

  • Wood DS, Jensen K, Crane A, Lee H, Dennis H, Gladwell J, Shurtz A, Fullwood DT, Seeley MK, Mitchell UH, Christensen WF, Bowden AE (2022) Accurate prediction of knee angles during open-chain rehabilitation exercises using a wearable array of nanocomposite stretch sensors. Sensors (Basel) 22(7):2499

    Article  CAS  PubMed  Google Scholar 

  • Wyles CC, Tibbo ME, Fu S, Wang Y, Sohn S, Kremers WK, Berry DJ, Lewallen DG, Maradit-Kremers H (2019) Use of natural language processing algorithms to identify common data elements in operative notes for total hip arthroplasty. J Bone Joint Surg Am 101(21):1931–1938

    Article  PubMed  Google Scholar 

  • Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S et al (2021) Artificial intelligence: a powerful paradigm for scientific research. Innovation (Cambridge (Mass)) 2(4):100179

    Google Scholar 

  • Yan BP, Lai WHS, Chan CKY, Au ACK, Freedman B, Poh YC, Poh MZ (2020) High-throughput, contact-free detection of atrial fibrillation from video with deep learning. JAMA Cardiol 5(1):105–107

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Oslo Sports Trauma Research Center, The Norwegian School of Sports Sciences, Oslo, Norway

    Seikai Toyooka, Andreas Persson, Lars Engebretsen & R. Kyle Martin

  2. Department of Orthopaedic Surgery, Teikyo University, Tokyo, Japan

    Seikai Toyooka

  3. Department of Orthopedic Surgery and Sports Medicine, Hospital for Special Surgery, New York, NY, USA

    Ayoosh Pareek

  4. Department of Orthopaedic Surgery, Oslo University Hospital, Oslo, Norway

    Andreas Persson & Lars Engebretsen

  5. Faculty of Medicine, University of Oslo, Oslo, Norway

    Lars Engebretsen

  6. Department of Orthopedic, University of Minnesota, Minneapolis, MN, USA

    R. Kyle Martin

  7. Department of Orthopedic Surgery, CentraCare, Saint Cloud, MN, USA

    R. Kyle Martin

Authors
  1. Seikai Toyooka
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  2. Ayoosh Pareek
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  3. Andreas Persson
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  4. Lars Engebretsen
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  5. R. Kyle Martin
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Editor information

Editors and Affiliations

  1. Dept. Orthopedics & Traumatology, Hacettepe University Fac. Medicine, Ankara, Türkiye

    Mahmut Nedim Doral

  2. Gothenburg, Sweden

    Jon Karlsson

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Toyooka, S., Pareek, A., Persson, A., Engebretsen, L., Martin, R.K. (2024). Artificial Intelligence and Healthcare Decision-Making. In: Doral, M.N., Karlsson, J. (eds) Sports Injuries. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36801-1_316-1

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  • DOI: https://doi.org/10.1007/978-3-642-36801-1_316-1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36801-1

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