ArticleIEEE transactions on bio-medical engineering2021
Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 56 citations in OpenAlex.
- Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters analysis.World journal of psychiatry · 2025Trial
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Tracking the eyes to track decline: Global eye movement similarity as a novel neuropsychological marker of mild cognitive impairment.GeroScience · 2026Article
- Enhancing Mild Cognitive Impairment Auxiliary Identification Through Multimodal Cognitive Assessment with Eye Tracking and Convolutional Neural Network Analysis.Biomedicines · 2025Article
- Multimodal Digital Phenotyping of Behavior in a Neurology Clinic: Development of the Neurobooth Platform and the First Two Years of Data Collection.medRxiv : the preprint server for health sciences · 2025Article
- Illustration image style transfer method design based on improved cyclic consistent adversarial network.PloS one · 2025Article
- Mild cognitive impairment detection from facial video interviews by applying spatial-to-temporal attention module.Expert systems with applications · 2024Article
- A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.Current Alzheimer research · 2024Review
- ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment.Sensors (Basel, Switzerland) · 2023Article
- A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson's disease.Frontiers in neurology · 2023Article
- Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.Frontiers in psychology · 2023Review
- An investigation of privacy preservation in deep learning-based eye-tracking.Biomedical engineering online · 2022Article
- Article
- A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data.Frontiers in human neuroscience · 2022Article
- Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.Biosensors · 2021Article
- Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review.Dementia and geriatric cognitive disorders extraArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveAlzheimer'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.
methodsWe 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.
resultsMild 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.
conclusionThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.