Sports Medicine and Artificial Intelligence: A Primer

Ramkumar, Prem N.; Luu, Bryan C.; Haeberle, Heather S.; Karnuta, Jaret M.; Nwachukwu, Benedict U.; Williams, Riley J.

doi:10.1177/03635465211008648

Cited by 57 publications

(37 citation statements)

References 42 publications

(58 reference statements)

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“…The accuracy of ensemble ML methods is highest for back, knee, hips and wrist injuries, 0.727–0.876, and lowest for shoulder injuries, 0.644. This level of performance is similar to ML models in team sports with an accuracy of 0.70 39 or 0.75–0.829 40 in a systematic review of 11 studies with number of participants ranging from 25 to 363 participants. ML models are only one step in identifying potentially important variables to investigate as risk factors, preventative measures or possible interaction effects.…”

Section: Discussionsupporting

confidence: 61%

Health challenges and acute sports injuries restrict weightlifting training of older athletes

Huebner

2022

BMJ Open Sport Exerc Med

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ObjectivesTo quantify acute injuries sustained during weightlifting that result in training restrictions and identify potential risk factors or preventative factors in Master athletes and to evaluate potentially complex interactions of age, sex, health-related and training-related predictors of injuries with machine learning (ML) algorithms.MethodsA total of 976 Masters weightlifters from Australia, Canada, Europe and the USA, ages 35–88 (51.1% women), completed an online survey that included questions on weightlifting injuries, chronic diseases, sport history and training practices. Ensembles of ML algorithms were used to identify factors associated with acute weightlifting injuries and performance of the prediction models was evaluated. In addition, a subgroup of variables selected by six experts were entered into a logistic regression model to estimate the likelihood of an injury.ResultsThe accuracy of ML models predicting injuries ranged from 0.727 to 0.876 for back, hips, knees and wrists, but were less accurate (0.644) for shoulder injuries. Male Master athletes had a higher prevalence of weightlifting injuries than female Master athletes, ranging from 12% to 42%. Chronic inflammation or osteoarthritis were common among both men and women. This was associated with an increase in acute injuries.ConclusionsTraining-specific variables, such as choices of training programmes or nutrition programmes, may aid in preventing acute injuries. ML models can identify potential risk factors or preventative measures for sport injuries.

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Section: Discussionsupporting

confidence: 61%

Health challenges and acute sports injuries restrict weightlifting training of older athletes

Huebner

2022

BMJ Open Sport Exerc Med

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“…The consumer benefit that the government chooses to implement the regulatory strategy is [ 14 ]

where E G Y is the expectation. The consumer benefit that the government chooses to implement the nonregulatory strategy is

where b and k are the parameters.…”

Section: Consumer Group Identification and Analysis For Ice And Snow ...mentioning

confidence: 99%

“…As shown in Figure 1 , the factors influencing consumption intention of ice and snow sports are given. Behind the vigorous development of the ice and snow sports industry, there is always the problem of insufficient discussion among the consumer groups of ice and snow sports, and behind the growth of the macroeconomy, there is also the problem of the weak contribution of microbehavior research to the promotion and industrial development [ 14 , 15 ]. At the same time, identifying consumer groups for ice and snow sports operation not only needs the support of management theory, behavioral theory, and public management theory but also needs to conduct in-depth discussion on consumer behavior issues related to the microbehavioral field, which is the motivation of this research topic [ 16 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

Consumer Group Identification Algorithm for Ice and Snow Sports

Zhang

Wang

2022

Computational Intelligence and Neuroscience

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As an important part of the modern sports industry system, the quality and level of its development are related to whether China’s sports industry can successfully become a pillar industry of the national economy. Therefore, the development of the ice and snow sports industry is to promote the expansion of China’s sports industry scale high quality development of the national economy and an important way to build sports power. Participative sports consumption is the most important part of sports consumption and the development of the sports industry. The sports industry separated from participative sports consumption is water without source and tree without roots, while participative sports consumption demand is the power source of participative sports consumption. At present, there is no systematic and complete research on participation sports consumption demand. In order to understand the causes and demand state of residents’ participation sports consumption demand and provide entry points for enterprises to formulate marketing strategies, this study constructs an organic system with participation sports service products as consumption objects, centering on the demanding state of participation sports consumers. In the system, on the theory of supply and demand, under the guidance of consumption economics theory, adhere to the combination of theoretical research and empirical analysis, the combination of macroplanning and microdesign, the combination of qualitative analysis and quantitative analysis, through the empirical investigation and receipt collection of residents’ participation sports consumption demand, the use of systematic analysis, literature method, and survey method, through mathematical analysis, and other research methods, the paper explores the main causes and demand conditions of residents’ participation sports consumption demand in different consumption states and excavates the main causes and demand conditions of participating sports consumption demand in different consumption states under different sports levels.

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“…26,40 As the number of repetitions of training and testing increases, the more experience and learning is made by the algorithms and the more accurate the predictions become. 26,40…”

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confidence: 99%

“…29 Generally, this learning is performed on a training set derived from the initial complete data set, and final predictions are made on a separate testing set (the remaining portion of data not used to train the models). 29,40 The performance of the ML algorithms is then assessed through several metrics that are derived from the predictions made on the testing set. 26,40 As the number of repetitions of training and testing increases, the more experience and learning is made by the algorithms and the more accurate the predictions become.…”

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confidence: 99%

Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis

et al. 2022

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Background: The International Hip Outcome Tool 12-Item Questionnaire (IHOT-12) has been proposed as a more appropriate outcome assessment for hip arthroscopy populations. The extent to which preoperative patient factors predict achieving clinically meaningful outcomes among patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) remains poorly understood. Purpose: To determine the predictive relationship of preoperative imaging, patient-reported outcome measures, and patient demographics with achievement of the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB) for the IHOT-12 at a minimum of 2 years postoperatively. Study Design: Case-control study; Level of evidence, 3. Methods: Data were analyzed for consecutive patients who underwent hip arthroscopy for FAIS between 2012 and 2018 and completed the IHOT-12 preoperatively and at a minimum of 2 years postoperatively. Fifteen novel machine learning algorithms were developed using 47 potential demographic, clinical, and radiographic predictors. Model performance was evaluated with discrimination, calibration, decision-curve analysis and the brier score. Results: A total of 859 patients were identified, with 685 (79.7%) achieving the MCID, 535 (62.3%) achieving the PASS, and 498 (58.0%) achieving the SCB. For predicting the MCID, discrimination for the best-performing models ranged from fair to excellent (area under the curve [AUC], 0.69-0.89), although calibration was excellent (calibration intercept and slopes: –0.06 to 0.02 and 0.24 to 0.85, respectively). For predicting the PASS, discrimination for the best-performing models ranged from fair to excellent (AUC, 0.63-0.81), with excellent calibration (calibration intercept and slopes: 0.03-0.18 and 0.52-0.90, respectively). For predicting the SCB, discrimination for the best-performing models ranged from fair to good (AUC, 0.61-0.77), with excellent calibration (calibration intercept and slopes: –0.08 to 0.00 and 0.56 to 1.02, respectively). Thematic predictors for failing to achieve the MCID, PASS, and SCB were presence of back pain, anxiety/depression, chronic symptom duration, preoperative hip injections, and increasing body mass index (BMI). Specifically, thresholds associated with lower likelihood to achieve a clinically meaningful outcome were preoperative Hip Outcome Score–Activities of Daily Living <55, preoperative Hip Outcome Score–Sports Subscale >55.6, preoperative IHOT-12 score ≥48.5, preoperative modified Harris Hip Score ≤51.7, age >41 years, BMI ≥27, and preoperative α angle >76.6°. Conclusion: We developed novel machine learning algorithms that leveraged preoperative demographic, clinical, and imaging-based features to reliably predict clinically meaningful improvement after hip arthroscopy for FAIS. Despite consistent improvements after hip arthroscopy, meaningful improvements are negatively influenced by greater BMI, back pain, chronic symptom duration, preoperative mental health, and use of hip corticosteroid injections.

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Sports Medicine and Artificial Intelligence: A Primer

Cited by 57 publications

References 42 publications

Health challenges and acute sports injuries restrict weightlifting training of older athletes

Health challenges and acute sports injuries restrict weightlifting training of older athletes

Consumer Group Identification Algorithm for Ice and Snow Sports

Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis

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