scholarly journals Artificial intelligence and data processing in injury diagnosis and prevention in competitive sports: A literature review

2021 ◽  
Vol 13 (2) ◽  
pp. 34-37
Author(s):  
Panagiotis Poulios ◽  
Athanasios Serlis ◽  
Peter P Groumpos ◽  
Ioannis Gliatis
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Data Processing ◽  
Injury Risk ◽  
Team Sports ◽  
Competitive Sports ◽  
Sports Performance

Artificial intelligence (AI) application opens an exciting perspective for predicting injury risk and team sports performance. A better understanding of the techniques of AI employed and of the sports that are using AI is warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sports performance and injury risk

scholarly journals Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
João Gustavo Claudino ◽  
Daniel de Oliveira Capanema ◽  
Thiago Vieira de Souza ◽  
Julio Cerca Serrão ◽  
Adriano C. Machado Pereira ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Risk Assessment ◽  
Performance Prediction ◽  
Injury Risk ◽  
Team Sports ◽  
And Performance

scholarly journals Individualization of Intensity Thresholds on External Workload Demands in Women’s Basketball by K-Means Clustering: Differences Based on the Competitive Level

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 324
Author(s):  
Sergio J. Ibáñez ◽  
Carlos D. Gómez-Carmona ◽  
David Mancha-Triguero
Keyword(s):  
Injury Risk ◽  
Team Sports ◽  
Ultra Wideband ◽  
Sports Performance ◽  
Basketball Players ◽  
Professional Basketball ◽  
Women's Basketball ◽  
Indoor Tracking ◽  
Tracking Technology ◽  
Basketball Teams

In previous studies found in the literature speed (SP), acceleration (ACC), deceleration (DEC), and impact (IMP) zones have been created according to arbitrary thresholds without considering the specific workload profile of the players (e.g., sex, competitive level, sport discipline). The use of statistical methods based on raw data could be considered as an alternative to be able to individualize these thresholds. The study purposes were to: (a) individualize SP, ACC, DEC, and IMP zones in two female professional basketball teams; (b) characterize the external workload profile of 5 vs. 5 during training sessions; and (c) compare the external workload according to the competitive level (first vs. second division). Two basketball teams were recorded during a 15-day preseason microcycle using inertial devices with ultra-wideband indoor tracking technology and microsensors. The zones of external workload variables (speed, acceleration, deceleration, impacts) were categorized through k-means clusters. Competitive level differences were analyzed with Mann–Whitney’s U test and with Cohen’s d effect size. Five zones were categorized in speed (<2.31, 2.31–5.33, 5.34–9.32, 9.33–13.12, 13.13–17.08 km/h), acceleration (<0.50, 0.50–1.60, 1.61–2.87, 2.88–4.25, 4.26–6.71 m/s2), deceleration (<0.37, 0.37–1.13, 1.14–2.07, 2.08–3.23, 3.24–4.77 m/s2), and impacts (<1, 1–2.99, 3–4.99, 5–6.99, 7–10 g). The women’s basketball players covered 60–51 m/min, performed 27–25 ACC-DEC/min, and experienced 134–120 IMP/min. Differences were found between the first and second division teams, with higher values in SP, ACC, DEC, and IMP in the first division team (p < 0.03; d = 0.21–0.56). In conclusion, k-means clustering can be considered as an optimal tool to categorize intensity zones in team sports. The individualization of external workload demands according to the competitive level is fundamental for designing training plans that optimize sports performance and reduce injury risk in sport.

Eco-Dynamics Approach to the study of Team Sports Performance~!2009-07-05~!2009-11-01~!2010-04-29~!

2010 ◽  
Vol 3 (1) ◽  
pp. 56-57 ◽  
Author(s):  
B. Travassos ◽  
D. Araujo ◽  
V. Correia ◽  
P. Esteves
Keyword(s):  
Team Sports ◽  
Sports Performance

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

Sports Medicine and Artificial Intelligence: A Primer

2021 ◽  
pp. 036354652110086
Author(s):  
Prem N. Ramkumar ◽  
Bryan C. Luu ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Benedict U. Nwachukwu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Sports Medicine ◽  
Injury Risk ◽  
Industrial Revolution ◽  
Improve Patient Care ◽  
Great Promise ◽  
Patient Reported ◽  
Athlete Injury ◽  
Improve Patient ◽  
Applications Of Ai

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

scholarly journals The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1317
Author(s):  
Maria Elena Laino ◽  
Angela Ammirabile ◽  
Alessandro Posa ◽  
Pierandrea Cancian ◽  
Sherif Shalaby ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Diagnostic Imaging ◽  
Clinical Work ◽  
Rapid Expansion ◽  
Chest Imaging ◽  
Imaging Data ◽  
Prognostic Information ◽  
Quantitative Ct ◽  
Work Up

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

scholarly journals Effects of competitive sports on temporomandibular dysfunction: a literature review

2020 ◽  
Vol 25 (1) ◽  
pp. 55-65
Author(s):  
Hannah Charlotte Freiwald ◽  
Nico Peter Schwarzbach ◽  
Anne Wolowski
Keyword(s):  
Literature Review ◽  
Athletic Performance ◽  
Anabolic Steroids ◽  
German Society ◽  
Competitive Sports ◽  
Inclusion Criteria ◽  
Temporomandibular Dysfunction ◽  
Competitive Athletes ◽  
Methodological Procedures ◽  
Everyday Stress

Abstract Objectives The German Society of Craniomandibular Function and Disorders recommends that patients suffering from temporomandibular dysfunctions should practice sports in order to compensate for everyday stress. This raises the question as to what extent competitive athletes develop temporomandibular dysfunctions or whether their athletic activities protect them. With the present literature review, the authors intend to give an overview of the currently available publications on this topic. Materials and methods A literature research in the PubMed and Google Scholar databases was performed to filter out the currently available publications on the topic ‚sports, and temporomandibular dysfunction. Results Out of 114 available articles, seven met the inclusion criteria. Two other relevant articles were found in the list of references, so that in total, nine publications were picked for the review. In case numbers ranging from eight to 347 subjects, a temporomandibular dysfunction was detected with a prevalence between 11.7% and 100% for athletes and between 11.11% and 14.3% for non-athletes. Different kinds of sports were evaluated, all of them contact sports: basketball, handball, wrestling, boxing, karate, mixed martial arts, field hockey, water polo, and soccer. One study compared athletes with and without consumption of anabolic steroids, regardless of the type of sport. The level of athletic performance varied across the different studies. Conclusions Currently, studies dealing with the effect of competitive sports on temporomandibular dysfunction are scarce. Inconsistent methodological procedures permit only limited comparability. Clinical relevance A general trend, however, can already be discerned: professional athletes suffer from temporomandibular dysfunctions more frequently than non-athletes.

scholarly journals Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Heliyon ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e06626
Author(s):  
Paulina Cecula ◽  
Jiakun Yu ◽  
Fatema Mustansir Dawoodbhoy ◽  
Jack Delaney ◽  
Joseph Tan ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Literature Review ◽  
Patient Flow ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
Inpatient Units ◽  
Improve Patient

Do We Share the Same View? Validating the Shared Mental Models in Team Sports Questionnaire

2021 ◽  
Vol 128 (2) ◽  
pp. 831-850
Author(s):  
Charlotte Raue ◽  
Dennis Dreiskaemper ◽  
Bernd Strauss
Keyword(s):  
Mental Models ◽  
Team Sports ◽  
Positive Influence ◽  
Shared Mental Models ◽  
Model Fit ◽  
Sports Performance ◽  
Measurement Instruments ◽  
Team Members ◽  
Confirmatory Factor ◽  
Good Internal Consistency

Shared mental models (SMMs) can exert a positive influence on team sports performance because team members with SMMs share similar tasks and team-related knowledge. There is currently insufficient sports research on SMMs because the underlying theory has not been adapted adequately to the sports context, and different SMMs measurement instruments have been used in past studies. In the present study we aimed to externally validate and determine the construct validity of the “Shared Mental Models in Team Sports Questionnaire” (SMMTSQ). Moreover, we critically examined the theoretical foundation for this instrument. Participants were 476 active team athletes from various sports. While confirmatory factor analysis did not support the SMMTSQ’s hierarchical model, its 13 subfactors showed a good model fit in an explorative correlative approach, and the model showed good internal consistency and item–total correlations. Thus, the instrument’s subfactors can be applied individually, even while there are remaining questions as to whether other questionnaires of this kind are an appropriate means of measuring SMMs in sport.

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