AIMC Topic: Eye Movements

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A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...

Classification of schizophrenia based on RAnet-ET: resnet based attention network for eye-tracking.

Journal of neural engineering
There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classificat...

The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

PloS one
BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product...

LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.

Behavior research methods
Deep learning methods have significantly advanced the field of gaze estimation, yet the development of these algorithms is often hindered by a lack of appropriate publicly accessible training datasets. Moreover, models trained on the few available da...

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents ...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Eye-gesture control of computer systems via artificial intelligence.

F1000Research
BACKGROUND: Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition, enabling intuitive control for users and accessibility for individuals with physical impairments.

High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.

Biosensors
An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work s...

Ocular-induced abnormal head postures: A systematic review and analysis.

Survey of ophthalmology
Abnormal head postures (AHPs) are frequently adopted as compensatory mechanisms by individuals affected by various ocular diseases to optimize the utilization of their visual field or alleviate symptoms such as diplopia. We review the causal relation...

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

JMIR formative research
BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions ...