Using artificial neural networks to identify patients with concussion and postconcussion syndrome based on antisaccades

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OBJECTIVE

Artificial neural networks (ANNs) have shown considerable promise as decision support tools in medicine, including neurosurgery. However, their use in concussion and postconcussion syndrome (PCS) has been limited. The authors explore the value of using an ANN to identify patients with concussion/PCS based on their antisaccade performance.

METHODS

Study participants were prospectively recruited from the emergency department and head injury clinic of a large teaching hospital in Toronto. Acquaintances of study participants were used as controls. Saccades were measured using an automated, portable, head-mounted device preprogrammed with an antisaccade task. Each participant underwent 100 trials of the task and 11 saccade parameters were recorded for each trial. ANN analysis was performed using the MATLAB Neural Network Toolbox, and individual saccade parameters were further explored with receiver operating characteristic (ROC) curves and a logistic regression analysis.

RESULTS

Control (n = 15), concussion (n = 32), and PCS (n = 25) groups were matched by age and level of education. The authors examined 11 saccade parameters and found that the prosaccade error rate (p = 0.04) and median antisaccade latency (p = 0.02) were significantly different between control and concussion/PCS groups. When used to distinguish concussion and PCS participants from controls, the neural networks achieved accuracies of 67% and 72%, respectively. This method was unable to distinguish study patients with concussion from those with PCS, suggesting persistence of eye movement abnormalities in patients with PCS. The authors’ observations also suggest the potential for improved results with a larger training sample.

CONCLUSIONS

This study explored the utility of ANNs in the diagnosis of concussion/PCS based on antisaccades. With the use of an ANN, modest accuracy was achieved in a small cohort. In addition, the authors explored the pearls and pitfalls of this novel approach and identified important future directions for this research.

ABBREVIATIONS ANN = artificial neural network; AS = antisaccade; AUC = area under the curve; MLP = multilayer perceptron; mTBI = mild TBI; NPV = negative predictive value; PCS = postconcussion syndrome; PER = prosaccade error rate; PPV = positive predictive value; PS = prosaccade; ROC = receiver operating characteristic; SMH = St. Michael’s Hospital; TBI = traumatic brain injury.
Article Information

Contributor Notes

Correspondence Alexander P. Landry: St. Michael’s Hospital, Toronto, ON, Canada. alex.landry@mail.utoronto.ca.INCLUDE WHEN CITING Published online November 23, 2018; DOI: 10.3171/2018.6.JNS18607.Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.

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