ReviewSensors (Basel, Switzerland)2023
Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.Biosensors · 2025Pooled it
- Recent advances (2022-2024) in eye-tracking for Parkinson's disease: a promising tool for diagnosing and monitoring symptoms.Frontiers in aging neuroscience · 2025Pooled it
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
- Advances in ocular motor and pupil biomarkers for neurological disorders.Brain communications · 2026Review
- From Mechanical Instability to Virtual Precision: Digital Twin Validation for Next-Generation MEMS-Based Eye-Tracking Systems.Sensors (Basel, Switzerland) · 2025Article
- Quantifying Mild Cognitive Impairments in Older Adults Using Multi-modal Wearable Sensor Data in a Kitchen Environment.medRxiv : the preprint server for health sciences · 2025Article
- Eye Tracking in Parkinson's Disease: A Review of Oculomotor Markers and Clinical Applications.Brain sciences · 2025Review
- Eye-tracking biomarkers of clinical expertise in ECG interpretation: statistical and machine learning evidence.Frontiers in medicine · 2025Article
- Construction of a prediction model for Alzheimer's disease using an AI-driven eye-tracking task on mobile devices.Aging clinical and experimental research · 2024Article
- Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.Sensors (Basel, Switzerland) · 2024Article
- EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning.Computational and structural biotechnology journal · 2024Article
- Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP.Sensors (Basel, Switzerland) · 2024Article
- Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases.Sensors (Basel, Switzerland) · 2024Review
- The effect of Immersive Virtual Reality on balance: an exploratory study on the feasibility of head-mounted displays for balance evaluation.Scientific reports · 2024Article
- Advancements in eye movement measurement technologies for assessing neurodegenerative diseases.Frontiers in digital health · 2024Review
- Detecting Abnormal Eye Movements in Patients with Neurodegenerative Diseases - Current Insights.Eye and brain · 2024Review
- Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.Frontiers in psychology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer's (AD) and Parkinson's diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.
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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.