IEEE transactions on bio-medical engineering · 2020
Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Haque Rafi U, Pongos Alvince L, Manzanares Cecelia M, Lah James J, Levey Allan I, Clifford Gari D
Аннотация
OBJECTIVE: Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task.
METHODS: We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals.
RESULTS: Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking.
CONCLUSION: This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment.
SIGNIFICANCE: Given the ubiquity of tablet devices, our approach has the potential to scale globally.
METHODS: We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals.
RESULTS: Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking.
CONCLUSION: This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment.
SIGNIFICANCE: Given the ubiquity of tablet devices, our approach has the potential to scale globally.