Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients

• DOI: 10.1371/journal.pone.0214525
• Publication Date: 2019-04-02T13:34:51Z
• Authors: Rosa M. S. Visscher; Nina Feddermann-D...; Fausto Romano; Dominik Straumann; Giovanni Bertolini
• Keywords: Adult; Male; Adolescent; Science; 11558 Neuroscience Center Zurich; Vision Disorders; 610 Medicine & health; 1100 General Agricultural and Biological Sciences; Neuropsychological Tests; Dizziness; Young Adult; 03 medical and health sciences; 0302 clinical medicine; 1300 General Biochemistry, Genetics and Molecular Biology; Artificial Intelligence; 11554 Zurich Center for Integrative Human Physiology (ZIHP); Cluster Analysis; Humans; Diagnosis, Computer-Assisted; Postural Balance; Brain Concussion; Retrospective Studies; 1000 Multidisciplinary; Post-Concussion Syndrome; Q; R; Headache; 10040 Clinic for Neurology; Vestibular Diseases; Medicine; Female; Vestibule, Labyrinth; Research Article; Sports

ISSN:1932-6203<br/>Objectives We 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). Methods Diagnostic 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. Results A 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). Conclusion The 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.<br/>PLoS ONE, 14 (4)<br/>