Eye Tracking Saliency Model for Neurobehavioral Disorder Screening
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Solution Overview
Problem
Current methods for diagnosing neurobehavioural disorders like Parkinson's disease, Alzheimer's disease, and ADHD are limited by their reliance on specific tasks and instructions, which can lead to contradictory results and are not suitable for large-scale screening or natural attention assessment.
Innovation Solution
A method and system that use eye-tracking and computational models to assess neurobehavioural disorders by monitoring eye movements during free viewing of visual scenes, generating saliency maps, and quantifying differences in attentional selection mechanisms without requiring specific tasks or instructions, allowing for classification of disorders based on natural attentional allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specific tasks and instructions are used to assess neurobehavioural disorders, then particular impairments in attentional allocation can be dissected, but the results become contradictory and difficult to use for identification of underlying behavioural differences between diseases
Solution Approach 1:
The system allows subjects to freely view visual scenes without requiring them to perform specific tasks or follow instructions. The eye-tracking system automatically records and analyzes natural eye movement patterns, eliminating the need for subject cooperation in structured tasks and reducing variability caused by task requirements.
Solution Approach 2:
The system transitions from measuring attentional deficits through multiple different task conditions to measuring a single parameter - natural eye movement patterns during free viewing. This parameter change simplifies the measurement approach and eliminates contradictions arising from different task conditions.
2Adaptability or versatility
If multiple different task conditions are used to assess attentional allocation, then various aspects of neurobehavioural disorders can be explored, but processing time increases and results become contradictory
Solution Approach 1:
The system uses a single free-viewing eye-tracking protocol that can assess multiple neurobehavioural disorders (Parkinson's disease, Alzheimer's disease, ADHD, schizophrenia, autism, Tourette syndrome, and progressive supranuclear palsy) simultaneously. This universal approach eliminates the need to repeat assessments across different task conditions for each disorder.
3Measurement precision
If structured visual and cognitive tasks are required, then specific behavioural impairments can be measured, but the tasks are not suitable for large-scale screening and natural attention assessment
Solution Approach 1:
The system automatically records and analyzes eye movement patterns during natural viewing without requiring subject cooperation in structured tasks. This automation enables large-scale screening while maintaining precise detection of behavioural differences through objective eye movement metrics.
Solution Approach 2:
The system replaces complex structured cognitive tasks with automated eye-tracking measurement. Instead of requiring subjects to perform and report on cognitive tasks, the system uses objective eye movement recording and computational analysis to infer attentional and behavioural characteristics.
Data Source
AI summary
This invention provides methods, system, and apparatus for assessing and/or diagnosing a neurobehavioural disorder in a subject. The methods, systems, and apparatus include the subject freely observing a visual scene, without having to carry out a task or follow specific instructions. In one embodiment, a computational model is used to select one or more feature in a visual scene and generate a spatial map having first map values that are predictive of eye movement end points of a hypothetical observer relative to the one or more feature. A subject's eye movements are recorded while the subject freely observes the visual scene, and a difference between second map values that correspond to the subject's eye movement endpoints and a set of map values selected randomly from the first map values is quantified, wherein the difference is indicative of a neurobehavioural disorder in the subject. Neurobehavioural disorders such as Parkinson's disease, Alzheimer's disease, Huntington's disease, fetal alcohol spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, autism, Tourette syndrome, and progressive supranuclear palsy may be assessed and/or diagnosed.