Paper Info
Title
Video-Based Eye Tracking Performance For Computer-Assisted Diagnostic Support Of Diabetic Neuropathy
Abstract
Diabetes is currently one of the major public health threats. The essential components for effective treatment of diabetes include early diagnosis and regular monitoring. However, health-care providers are often short of human resources to closely monitor populations at risk. In this work, a video-based eye-tracking method is proposed as a low-cost alternative for detection of diabetic neuropathy. The method is based on the tracking of the eye-trajectories recorded on videos while the subject follows a target on a screen, forcing saccadic movements. Upon extraction of the eye trajectories, representation of the obtained time-series is made with the help of heteroscedastic ARX (H-ARX) models, which capture the dynamics and latency on the subject?s response, while features based on the H-ARX model?s predictive ability are subsequently used for classification. The methodology is evaluated on a population constituted by 11 control and 20 insulin-treated diabetic individuals suffering from diverse diabetic complications including neuropathy and retinopathy. Results show significant differences on latency and eye movement precision between the populations of control subjects and diabetics, while simultaneously demonstrating that both groups can be classified with an accuracy of 95%. Although this study is limited by the small sample size, the results align with other findings in the literature and encourage further research.
Year
DOI
Venue
2021
10.1016/j.artmed.2021.102050
ARTIFICIAL INTELLIGENCE IN MEDICINE
Keywords
DocType
Volume
Video-based eye tracking, Diabetic neuropathy, Computer-assisted diagnosis, Heteroscedastic ARX (H-ARX) models
Journal
114
ISSN
Citations 
PageRank 
0933-3657
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Luis David Avendaño-Valencia100.34
Knud Bonnet Yderstraede220.97
Esmaeil S. Nadimi395.90
Victoria Blanes-Vidal463.46