ArticlePloS one2019
Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06473883 (Effects of Vestibulo-oculomotor and Neuromuscular Training on the Incidence of Concussions and Musculoskeletal Injuries in Competitive Female Football Players), which is not on this map. Cited by 12 papers.
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Effects of Vestibulo-oculomotor and Neuromuscular Training on the Incidence of Concussions and Musculoskeletal Injuries in Competitive Female Football Players
Who cites it
12 citing papers in PubMed.
- Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning.Annals of biomedical engineering · 2025Article
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
- Biomechanical induction of mild brain trauma in larval zebrafish: effects on visual startle reflex habituation.Brain communications · 2023Article
- Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea).Scientific reports · 2022Article
- A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders.Biology · 2022Review
- Article
- Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion.Frontiers in sports and active living · 2022Article
- Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2021Article
- Artificial intelligence as an emerging technology in the current care of neurological disorders.Journal of neurology · 2021Review
- Machine Learning for Subtyping Concussion Using a Clustering Approach.Frontiers in human neuroscience · 2021Article
- Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.Journal of neurotrauma · 2020Article
- Derivation and Validation of Subtypes of Concussion: A Systematic Review.Neurotrauma reportsReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe propose a bottom-up, machine-learning approach, for the objective vestibular and balance diagnostic data of concussion patients, to provide insight into the differences in patients' phenotypes, independent of existing diagnoses (unsupervised learning).
methodsDiagnostic data from a battery of validated balance and vestibular assessments were extracted from the database of the Swiss Concussion Center. The desired number of clusters within the patient database was estimated using Calinski-Harabasz criteria. Complex (self-organizing map, SOM) and standard (k-means) clustering tools were used, and the formed clusters were compared.
resultsA total of 96 patients (81.3% male, age (median [IQR]): 25.0[10.8]) who were expected to suffer from sports-related concussion or post-concussive syndrome (52[140] days between diagnostic testing and the concussive episode) were included. The cluster evaluation indicated dividing the data into two groups. Only the SOM gave a stable clustering outcome, dividing the patients in group-1 (n = 38) and group-2 (n = 58). A large significant difference was found for the caloric summary score for the maximal speed of the slow phase, where group-1 scored 30.7% lower than group-2 (27.6[18.2] vs. 51.0[31.0]). Group-1 also scored significantly lower on the sensory organisation test composite score (69.0[22.3] vs. 79.0[10.5]) and higher on the visual acuity (-0.03[0.33] vs. -0.14[0.12]) and dynamic visual acuity (0.38[0.84] vs. 0.20[0.20]) tests. The importance of caloric, SOT and DVA, was supported by the PCA outcomes. Group-1 tended to report headaches, blurred vision and balance problems more frequently than group-2 (>10% difference).
conclusionThe SOM divided the data into one group with prominent vestibular disorders and another with no clear vestibular or balance problems, suggesting that artificial intelligence might help improve the diagnostic process.
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