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Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care

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A Letter to the Editor to this article was published on 14 October 2022

A Letter to the Editor to this article was published on 14 October 2022

A Letter to the Editor to this article was published on 23 July 2022

A Letter to the Editor to this article was published on 23 July 2022

Abstract

There is growing interest in the role of predictive analytics in sport, where such extensive data collection provides an exciting opportunity for the development and utilisation of prediction models for medical and performance purposes. Clinical prediction models have traditionally been developed using regression-based approaches, although newer machine learning methods are becoming increasingly popular. Machine learning models are considered 'black box'. In parallel with the increase in machine learning, there is also an emergence of proprietary prediction models that have been developed by researchers with the aim of becoming commercially available. Consequently, because of the profitable nature of proprietary systems, developers are often reluctant to transparently report (or make freely available) the development and validation of their prediction algorithms; the term 'black box' also applies to these systems. The lack of transparency and unavailability of algorithms to allow implementation by others of ‘black box’ approaches is concerning as it prevents independent evaluation of model performance, interpretability, utility, and generalisability prior to implementation within a sports medicine and performance environment. Therefore, in this Current Opinion article, we: (1) critically examine the use of black box prediction methodology and discuss its limited applicability in sport, and (2) argue that black box methods may pose a threat to delivery and development of effective athlete care and, instead, highlight why transparency and collaboration in prediction research and product development are essential to improve the integration of prediction models into sports medicine and performance.

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References

  1. Horvat T, Job J. The use of machine learning in sport outcome prediction: a review. Wiley Interdiscipl Rev Data Min Knowl Discov. 2020;10(5):e1380.

    Google Scholar 

  2. McCall A, Fanchini M, Coutts AJ. Prediction: the modern-day sport-science and sports-medicine “quest for the holy grail.” Int J Sports Physiol Perform. 2017;12(5):704–6.

    Article  PubMed  Google Scholar 

  3. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Int Med. 2015;162(1):W1–73.

    Article  PubMed  Google Scholar 

  4. Riley RD, van der Windt D, Croft P, Moons KG. Prognosis research in healthcare: concepts, methods, and impact. Oxford University Press; 2019.

    Book  Google Scholar 

  5. Hughes T, Sergeant JC, van der Windt DA, Riley R, Callaghan MJ. Periodic health examination and injury prediction in professional football (Soccer): theoretically, the prognosis is good. Sport Med. 2018;48(11):2443–8.

    Article  Google Scholar 

  6. Riley RD, Hayden JA, Steyerberg EW, Moons KG, Abrams K, Kyzas PA, et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med. 2013;10(2):e1001380.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Van Calster B, Wynants L, Timmerman D, Steyerberg EW, Collins GS. Predictive analytics in health care: how can we know it works? J Am Med Inf Assoc. 2019;26(12):1651–4.

    Article  Google Scholar 

  9. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

    Article  CAS  PubMed  Google Scholar 

  10. Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138–60.

    Article  Google Scholar 

  11. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–15.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Dhiman P, Ma J, Navarro CA, Speich B, Bullock G, Damen JA, et al. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol. 2021;138:60–72.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Da Cruz HF, Pfahringer B, Martensen T, Schneider F, Meyer A, Böttinger E, et al. Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology. ArtifIntell Med. 2021;111:101982.

    Google Scholar 

  14. Cook C. Predicting future physical injury in sports: it's a complicated dynamic system. Br J Sport Med. 2016;50(22):1356–7.

    Article  Google Scholar 

  15. Shah ND, Steyerberg EW, Kent DM. Big data and predictive analytics: recalibrating expectations. JAMA. 2018;320(1):27–8.

    Article  PubMed  Google Scholar 

  16. Van Calster B, Steyerberg EW, Collins GS. Artificial intelligence algorithms for medical prediction should be nonproprietary and readily available. JAMA Int Med. 2019;179(5):731.

    Article  Google Scholar 

  17. Seow D, Graham I, Massey A. Prediction models for musculoskeletal injuries in professional sporting activities: A systematic review. Trans Sports Med. 2020;3(6):505–17.

    Article  Google Scholar 

  18. Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14(1):40.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Watson DS, Krutzinna J, Bruce IN, Griffiths CE, McInnes IB, Barnes MR, et al. Clinical applications of machine learning algorithms: beyond the black box. Bmj. 2019;12;364.

    Google Scholar 

  20. Hernán MA, Hsu J, Healy B. A second chance to get causal inference right: a classification of data science tasks. Chance. 2019;32(1):42–9.

    Article  Google Scholar 

  21. Shmueli G. To explain or to predict? Stat Sci. 2010;25(3):289–310.

    Article  Google Scholar 

  22. Prosperi M, Guo Y, Sperrin M, Koopman JS, Min JS, He X, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell. 2020;2(7):369–75.

    Article  Google Scholar 

  23. Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC; 2020.

    Google Scholar 

  24. Sperrin M, Jenkins D, Martin GP, Peek N. Explicit causal reasoning is needed to prevent prognostic models being victims of their own success. J Am Med Informatic Assoc. 2019;26(12):1675–6.

    Article  Google Scholar 

  25. Hingorani AD, van der Windt DA, Riley RD, Abrams K, Moons KG, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Impellizzeri FM, McCall A, Ward P, Bornn L, Coutts AJ. Training load and its role in injury prevention, part 2: conceptual and methodologic pitfalls. J Athl Train. 2020;55(9):893–901.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Impellizzeri FM, Menaspà P, Coutts AJ, Kalkhoven J, Menaspa MJ. Training load and its role in injury prevention, part I: back to the future. J Athl Train. 2020;55(9):885–92.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Impellizzeri FM, Ward P, Coutts AJ, Bornn L, McCall A. Training load and injury part 1: the devil is in the detail—challenges to applying the current research in the training load and injury field. J Orthop Sport Phys Ther. 2020;50(10):574–6.

    Article  Google Scholar 

  29. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375.

    Article  PubMed  Google Scholar 

  30. Bzdok D, Altman N, Krzywinski M. Points of significance: statistics versus machine learning. Nature 2018;14(12):1119.

    Google Scholar 

  31. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol. 2016;76:175–82.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35(2):214–26.

    Article  PubMed  Google Scholar 

  33. Collins GS, Moons KG. Reporting of artificial intelligence prediction models. The Lancet. 2019;393(10181):1577–9.

    Article  Google Scholar 

  34. Steyerberg EW. Clinical prediction models. Springer; 2019.

    Book  Google Scholar 

  35. Wynants L, Collins GS, Van Calster B. Key steps and common pitfalls in developing and validating risk models. BJOG. 2017;124(3):423–32.

    Article  CAS  PubMed  Google Scholar 

  36. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19(4):453–73.

    Article  CAS  PubMed  Google Scholar 

  37. Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans M, Vergouwe Y, Habbema JDF. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–81.

    Article  CAS  PubMed  Google Scholar 

  38. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal–external, and external validation. J Clin Epidemiol. 2016;69:245–7.

    Article  PubMed  Google Scholar 

  39. Efron B, Tibshirani RJ. An introduction to the bootstrap. CRC Press; 1994.

    Book  Google Scholar 

  40. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. Bmj. 2020;25;368.

    Google Scholar 

  41. Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. bmj. 2020;20;368.

    Google Scholar 

  42. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216.

    Article  PubMed  PubMed Central  Google Scholar 

  43. D'Amour A, Heller K, Moldovan D, Adlam B, Alipanahi B, Beutel A, et al. Underspecification presents challenges for credibility in modern machine learning. arXiv preprint arXiv:201103395. 2020.

  44. Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98(9):691–8.

    Article  PubMed  Google Scholar 

  45. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–53.

    Article  CAS  PubMed  Google Scholar 

  46. Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Waldron L, Wang B, et al. Transparency and reproducibility in artificial intelligence. Nature. 2020;586(7829):E14–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Janssens A. Proprietary algorithms for polygenic risk: protecting scientific innovation or hiding the lack of it? Genes. 2019;10(6):448.

    Article  CAS  PubMed Central  Google Scholar 

  48. van Smeden M, de Groot JA, Moons KG, Collins GS, Altman DG, Eijkemans MJ, et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med Res Methodol. 2016;16(1):163.

    Article  PubMed  PubMed Central  Google Scholar 

  49. van Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455–74.

    Article  PubMed  Google Scholar 

  50. Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, et al. Minimum sample size for developing a multivariable prediction model: PART II—binary and time-to-event outcomes. Stat Med. 2019;38(7):1276–96.

    Article  PubMed  Google Scholar 

  51. Riley RD, Debray TP, Collins GS, Archer L, Ensor J, van Smeden M, et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med. 2021; 40(19):4230–51.

    Article  PubMed  Google Scholar 

  52. Snell KI, Archer L, Ensor J, Bonnett LJ, Debray TP, Phillips B, et al. External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. J Clin Epidemiol. 2021;135:79–89.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hughes T, Riley RD, Callaghan MJ, Sergeant JC. The value of preseason screening for injury prediction: the development and internal validation of a multivariable prognostic model to predict indirect muscle injury risk in elite football (soccer) players. Sports Med-Open. 2020;6(1):1–13.

    Article  Google Scholar 

  54. Jennings D, Cormack S, Coutts AJ, Boyd LJ, Aughey RJ. Variability of GPS units for measuring distance in team sport movements. Int J Sport Physiol Perform. 2010;5(4):565–9.

    Article  Google Scholar 

  55. Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports Med. 2013;43(9):773–81.

    Article  PubMed  Google Scholar 

  56. Wisbey B, Rattray B, Pyne D. Quantifying changes in AFL player game demands using GPS tracking: 2008 AFL season. Florey (ACT): FitSense Australia; 2008.

    Google Scholar 

  57. Me E, Unold O. Machine learning approach to model sport training. Comput Hum Behav. 2011;27(5):1499–506.

    Article  Google Scholar 

  58. Alderson J. A markerless motion capture technique for sport performance analysis and injury prevention: toward a ‘big data’, machine learning future. J Sci Med Sport. 2015;19:e79.

    Article  Google Scholar 

  59. Zelič I, Kononenko I, Lavrač N, Vuga V. Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries. J Med Syst. 1997;21(6):429–44.

    Article  PubMed  Google Scholar 

  60. Robertson S, Bartlett JD, Gastin PB. Red, amber, or green? Athlete monitoring in team sport: the need for decision-support systems. Int J Sport Physiol Perform. 2017;12(s2):S2-73-S2-9.

    Article  Google Scholar 

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Funding

GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant: C49297/A27294). No other sources of funding were used to assist in the preparation of this article.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Garrett S. Bullock

  2. Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK

    Garrett S. Bullock & Stefan Kluzek

  3. Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Garrett S. Bullock, Gary S. Collins & Stefan Kluzek

  4. Department of Health Professions, Manchester Metropolitan University, Manchester, UK

    Tom Hughes

  5. Manchester United Football Club, Manchester, UK

    Tom Hughes

  6. Red Bull Athlete Performance Center, Thalgua, Austria

    Amelia H. Arundale

  7. Icahn School of Medicine, Mount Sinai Health System, New York, NY, USA

    Amelia H. Arundale

  8. Seattle Seahawks, Seattle, WA, USA

    Patrick Ward

  9. Centre for Statistics in Medicine, University of Oxford, Oxford, UK

    Gary S. Collins

  10. Oxford University Hospitals NHS Foundation Trust, Oxford, UK

    Gary S. Collins

  11. University of Nottingham, Nottingham, UK

    Stefan Kluzek

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  1. Garrett S. Bullock
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  2. Tom Hughes
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  3. Amelia H. Arundale
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Contributions

GSB, TH, GSC and SK conceived the study idea. GSB, TH, GSC and SK were involved in design and planning. GSB, TH and SK wrote the first draft. GSB, TH, AHA, PW, GSC and SK critically appraised the manuscript. GSB, TH and SK wrote the first draft. GSB, TH, AHA, PW, GSC and SK approved the final version of the manuscript.

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Correspondence to Garrett S. Bullock.

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Garrett S. Bullock, Tom Hughes, Amelia H. Arundale, Patrick Ward, Gary S. Collins and Stefan Kluzek declare that they have no conflicts of interest relevant to the content of this article.

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Bullock, G.S., Hughes, T., Arundale, A.H. et al. Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care. Sports Med 52, 1729–1735 (2022). https://doi.org/10.1007/s40279-022-01655-6

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