# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
## 5 Discussion  
In the present study, we investigated the potential of using smooth pursuit eye movements (SPEM) to distinguish individuals with varying levels of cognitive impairment from healthy controls. Our goal was to design a simple language independent task that encouraged participants to move their eyes as naturally as possible while following a moving object. Our findings support previous research suggesting that SPEM performance is sensitive to cognitive decline [5,9]. In particular, we found that the mean squared error between the stimulus and the subject's gaze was significantly higher in individuals with cognitive impairment, especially during fast horizontal and slow vertical motion. These findings are consistent with prior studies that have demonstrated impairments in pursuit gain, increased saccadic intrusions, and delayed initiation in individuals with MCI [9] and AD [10]. To better illustrate these differences, we present examples of eye movement traces from two participants during these tasks. Figure 5(a,b) show the trace of a healthy control, while Figure 5(c,d) show the trace of an individual with possible Alzheimer's disease (AD). Comparing the two traces, it is clear that the individual with AD had more difficulties tracking the stimulus accurately. This was especially noticeable in the fast horizontal task (Figure 5(c)), where the subject struggled to follow the object across the screen. In the slow vertical task (Figure 5(d)), the trace suggests that the participant tried to predict the stimulus's position and \"jumped\" with their gaze ahead, rather than smoothly following its path. On the other hand, the healthy subject was mostly able to follow the stimulus smoothly in both tasks (Figure 5(a,b)). Of course, these are just two typical examples of the eye movement traces we recorded. Although we found consistent statistical differences between the groups in most of the pursuit tasks (see Figure 3), the overlap in how the results were spread out (see Figure 4(b)) suggests that SPEM alone might not be enough to clearly tell individuals apart for clinical use. Still, our model built using the logistic regression algorithm shows some promise. The model reached an AUC of 0.675, which is not high enough for diagnosis on its own, but it is a good starting point given how simple and non-invasive the SPEM task is. The ROCcurve showed that the model could pick up some cases of cognitive impairment with few false positives, which means it could be useful as part of a wider screening approach. Another interesting finding is that the pursuit accuracy worsened as the level of cognitive impairment increased. We saw this gradual change from HC, to borderline cases, to MCI, and to possible AD (see Figure 4). This suggests that SPEM might not only help with identifying cognitive problems, but could also be useful for tracking how the condition progresses or for spotting people at higher risk. The groups in our study were not age matched. However, the main goal here was to see whether eye-tracking during smooth pursuit tasks could pick up eye movement changes in people with cognitive decline compared to those without it, regardless of age. The study participants were recruited randomly and consecutively, which means we ended up with some age differences--something that was expected, since cognitive decline tends to occur more with age. The machine learning results were modest, which indicates that analysing only the SPEM would not be enough to effectively differentiate CI subjects from HC. However, it shows promise in being one of the possible tasks in a battery of different neuro-psychological tasks that could be performed using the eye gaze, like reading [12; 22]. In addition, our sample size was fairly small, and because the study was only conducted at one point in time, we cannot say much about how SPEM performance might change over time.