A logistic regression model for diagnosing Alzheimer's disease based on eye movement tracking technology

Abstract

Objective To establish a logistic regression model based on eye tracking technology to diagnose Alzheimer's disease (AD). Methods A total of 113 AD patients diagnosed in the clinic of memory of the Department of Geriatrics of the First Affiliated Hospital of Chongqing Medical University from January 2021 to November 2021 were recruited in this study, and another 54 individuals with normal cognition who taking physical examination during same period served as control group.All subjects were tested by eye tracker, and 5 indicators including saccades in the fixation task (resting state) were measured.Then, 70%(117) subjects were randomly included in the training set; and the rest 30%(50) subjects in the test set.After the above 5 indicators were used to predict AD in the training set, a logistic regression model was established by using SPSS software, and receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic efficacy of the model, which was then testified in the test set.The diagnostic factors with statistically significant results between the 2 subject groups were correlated respectively with the scores of free and cued selective reminding test (FCSRT), clock drawing task (CDT), digit span test (DST) and trail making test (TMT), auditory verbal learning test (AVLT), and Boston naming test (BNT) so as to study the possible mechanism. Results There were no statistical differences between the 2 groups regarding age, gender, education level, saccades in the fixation task and saccades in the pursuit task.But significant differences were observed between the 2 groups in saccade latency in the pro-saccade task, saccade latency in the anti-saccade task and error rate of anti-saccade task (all P < 0.001).A prediction model based on multivariate logistic regression was established and consisted of saccades in the fixation task, saccade latency in anti-saccade task and error rate of anti-saccade task in prediction of AD with an AUC value of 0.913 and 0.964 respectively in the training and test sets.In the experimental group, the anti-saccade latency was negatively correlated with the scores of FCSRT, TMT and AVLT, and the anti-saccade error rate was negatively correlated with the scores of FCSRT, CDT, AVLT and BNT. Conclusion Our established diagnostic model based on eye tracking data is of good prediction for the diagnosis of AD in clinical practice.

Keywords

• eye tracking technology
• alzheimer's disease
• diagnostic model