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

Abstract

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.

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

“…As we previously reported, Eo and Ec phybrata features were differentially predictive of clinical impairment [58], and the present NTS RF model results confirm this past work. The one published machine learning study relevant to Use Case 2 carried out a retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients using two different clustering tools (K-means and self-organizing map) [37], and demonstrated the presence of two distinct groups, one with prominent vestibular disorders and another with no clear vestibular or balance problems. However, this study did not include ROC analyses of diagnostic sensitivity, specificity, F1, or AUC.…”

“…Applications of machine learning to help automate concussion diagnoses and recovery monitoring have attracted significant research interest in recent years [30]. Data sources include structural and functional MRI [31], electroencephalography (EEG) [32][33][34][35][36], clinical scales [13,30,32,[37][38][39], balance and vestibular diagnostic data [37], gait analysis [30,40], eye tracking [41], blood biomarkers [42], analysis of head impact data [43][44][45], and a variety of wearable physiological sensors [33]. Wearable sensors are of particular interest because of their low cost, ease of use, and compatibility with remote patient monitoring.…”

“…Damage to the vestibular balance organs, vestibular nerve, and multiple connected neurological circuits are associated with many cognitive and physical symptoms arising from concussions [71,72], and damage to the vestibular system is often the predominant driver of long-term symptoms [73]. A recent ML cluster analysis of concussion patients revealed the presence of two distinct groups, one with prominent vestibular disorders and another with no clear vestibular or balance problems [37]. However, many concussion patients are still managed uniformly, despite the nature of their injuries, in the hopes that the pertinent physiological impairments will be addressed.…”

Application of artificial intelligence in the otoneurology diagnostic process – an overview

“…Przed podjęciem badań z zastosowaniem AI w klinice otoneurologii należy przeprowadzić konsultacje ze specjalistami dziedzin (neurologii, okulistyki, fizjoterapii) w celu wybrania właściwych narzędzi i metod badawczych [26]. Jak wynika z dotychczasowych obserwacji i badań zastosowanie sztucznej inteligencji wpływa korzystnie na proces diagnostyczny w otoneurologii.…”

Machine Learning for Subtyping Concussion Using a Clustering Approach

“…Recent AI work has used machine learning to predict symptom resolution following sport-related concussion (Bergeron et al, 2019 ). While another study used a clustering approach on vestibular and balance diagnostic data, and demonstrated two clinically distinct groups, patients with prominent vestibular disorders and others with no clear vestibular or balance impairment (Visscher et al, 2019 ). Specifically in consideration of the heterogeneity of concussion and noting the ways in which this complicates research efforts, Kenzie et al ( 2018 ) utilized causal loop diagramming to visualize relationships between concussion injury factors, including pathophysiology, deficits, symptom persistence and recovery trajectories.…”