**4. Conclusions**

In this paper, we presented some preliminary results on the analysis of VSST data, performed on three groups of individuals: patients affected by the extrapyramidal syndrome or by chronic pain symptoms and healthy subjects. Starting from the idea that the problem to be solved is multifaceted—which means that the data collected in a VSST have different nature and can be analysed from different viewpoints [22]—the goal of the present study is to detect if some regularities can be found within the data that allow to properly group them. Such detected differences could be potentially used in clinical practice, and therefore play an important role in evidencing possible neurological syndromes. The three-stage statistical analysis has been carried out on the basis of three metrics: the blinking rate, the maximum pupil size variation and the blinking average duration. The analysis showed the presence of some statistically significant differences between the groups analysed. In particular, the relevant difference in blinking rate between healthy and chronic patients is confirmed by each step of the analysis. Moreover, a statistical difference was detected between extrapyramidal and chronic patients for what concerns the maximum pupil size variation and blinking average duration. Conversely, based on the ETT (Eye-Tracking Trajectory) image dataset, a U-Net *ensemble* architecture was trained to reconstruct input images, using their latent representations, to appropriately cluster the visual data. Embeddings were, in fact, divided clearly into three separated groups. We performed preliminary testing, showing promising generalisation capabilities. Limitations of this work are mainly due to the small dataset available. Moreover variations of the VSST could be implemented and standardised, to avoid biases due to the fact that no instructions were given concerning the number of times the patients should have completed the sequence during the data acquisition time. Therefore, future research and extensions will concern new standardised data collection for further testing and a more extensive validation of the employed approaches based on a wider experimentation. For example a possible extension of the present study could be to consider more than three mutual exclusive classes, so as to include co-morbidities, i.e., cases in which additional conditions are concurrent to the primary one.

**Author Contributions:** Investigation, N.P., C.G., V.L. and G.M.D.; Conceptualisation and Methodology, N.P., C.G., V.L., M.B. and G.M.D.; Software, N.P., C.G., V.L. and G.M.D.; Supervision, M.L.S., M.B. and G.M.D.; Data Curation, E.S, ., N.P., C.G. and V.L.; Writing—original draft, N.P., C.G., V.L. and G.M.D.; and Writing—review and editing, N.P., C.G., V.L., M.L.S., M.B. and G.M.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** The patients' consent was waived due the anonymous nature of the data.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors wish to thank RoNeuro Institute, part of the Romanian Foundation for the Study of Nanoneurosciences and Neuroregeneration, Cluj-Napoca, Romania, represented by Dafin Fior Muresanu, for providing the datasets used here for the experiments. Alessandra Rufa of the Department of Medicine, Surgery and Neuroscience, at the University of Siena, and Dario Zanca of the Department of Artificial Intelligence in Biomedical Engineering, at the University of Erlangen-Nürnberg, are also gratefully acknowledged for the fruitful discussions done at different stages of the present work.

**Conflicts of Interest:** The authors declare no conflict of interest.
