Digital Visual Assessment System with Neural Network Adaptation
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Solution Overview
Problem
Current visual-based assessments for mental disorders in children and adults are limited by bias and manual errors, lacking the ability to utilize learning algorithms and neural networks for continuous improvement and accurate prediction of disabilities.
Innovation Solution
A method and apparatus that digitally measure and classify visual and visual motor responses to stimuli using a computerized device, employing learning algorithms and neural networks to improve profile analysis and predict potential disorders, with the ability to adjust condition correlation functions over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used for visual assessments, then ease of operation is maintained, but measurement precision deteriorates due to bias and manual errors
Solution Approach 1:
The patent replaces manual measurement methods with a computerized system that captures visual stimuli and responses using a display device and camera, then processes images through algorithms to automatically measure visual-motor integration. This substitution eliminates manual errors and bias while maintaining ease of operation through automated processing.
Solution Approach 2:
The patent introduces an intermediary computerized processing system that acts as a mediator between the visual stimulus presentation and the measurement of responses. This intermediary system captures images, processes them through algorithms, and generates measurements, thereby eliminating direct manual measurement errors while preserving operational simplicity.
2Adaptability or versatility
If static assessment instruments are used, then device complexity is minimized, but adaptability deteriorates as they cannot continuously improve or predict disabilities
Solution Approach 1:
The patent transforms the assessment system from a static instrument to a dynamic one by implementing machine learning algorithms that continuously learn from new data. The system adapts its measurement and classification capabilities over time, improving its ability to predict disabilities while managing complexity through modular algorithm design.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system's predictions and measurements are continuously refined based on new data input. The machine learning models receive feedback from additional assessments and clinical outcomes, allowing the system to adapt and improve its diagnostic accuracy while maintaining a manageable complexity through iterative refinement.
3Measurement precision
If simple classification methods are used, then device complexity is reduced, but measurement precision deteriorates in predicting potential disorders
Solution Approach 1:
The patent segments the classification task into multiple specialized machine learning models, each trained to detect specific visual-motor integration patterns associated with different disorders. This segmentation allows the system to achieve high measurement precision for specific conditions while managing overall complexity through modular model architecture and specialized processing for each disorder type.
Data Source
AI summary
A method for improving a profile analysis of an interpretive framework stored in a memory may include producing and displaying visual stimuli on a computerized device to test visual and visual motor responses of an individual subject in response to the displayed visual stimuli. The method may also include classifying and categorizing digitally measured visual and visual motor responses of the individual subject to the displayed visual stimuli. The method may further include continually modifying parameters of the profile analysis of the interpretive framework corresponding to at least one condition based at least in part on an item analysis corresponding to a pattern of performance determined during the classifying and categorizing of the digitally measured visual and visual motor responses of the individual subject.