Diabetes mellitus is a disease that may cause dysfunctions in the sympathetic and parasympathetic nervous system. Therefore, the pupillary reflex of diabetic patients shows characteristics that distinguish them from healthy people, such as pupil radius and contraction time. These features can be measured by the noninvasive way of dynamic pupillometry, and an analysis of the data can be used to check the existence of a neuropathy. In this paper, it is proposed the use of artificial neural networks for helping screening the diabetes occurrence through the dynamic characteristics of the pupil, with successful results. © 2011 Springer-Verlag.
CITATION STYLE
Yano, V., Ferrari, G., & Zimmer, A. (2011). Using the pupillary reflex as a diabetes occurrence screening aid tool through neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6754 LNCS, pp. 40–47). https://doi.org/10.1007/978-3-642-21596-4_5
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