Binocular Vision Assessment Using Dynamic Random Dot Stereograms
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Solution Overview
Problem
Conventional random dot stereogram-based diagnostic techniques for assessing fusional vergence and stereoacuity are prone to fusional locking, leading to inaccurate results due to pseudo fusion and neglecting individual differences in interpupillary distance, resulting in false positives and inadequate treatment prescriptions.
Innovation Solution
A system that generates and dynamically updates random dot stereograms with varying dot arrangements and sub-portion positions to prevent fusional locking, incorporating personalized interpupillary distance measurements for accurate assessment of fusional vergence and stereoacuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional random dot stereogram-based diagnostic techniques are used, then the assessment process is simple and quick, but the diagnostic accuracy deteriorates due to fusional locking and pseudo fusion
Solution Approach 1:
The system dynamically updates the random dot stereogram by changing dot arrangements, sub-portion positions, and interpupillary distance parameters between assessments. This dynamic adaptation prevents fusional locking by ensuring each assessment presents a novel visual configuration that cannot be pre-adapted to, thereby maintaining high diagnostic accuracy without requiring overly complex equipment.
Solution Approach 2:
The system changes key parameters including interpupillary distance (matching individual patient measurements), sub-portion displacement values, and random dot configurations between assessments. These parameter variations prevent pseudo-fusion effects while maintaining assessment simplicity through automated parameter management.
2Reliability
If fixed random dot stereogram presentations are used, then the assessment procedure is straightforward, but reliability deteriorates due to fusional locking and pseudo fusion
Solution Approach 1:
The system implements periodic reassessment with updated stereogram configurations. Between assessments, the random dot patterns, sub-portion positions, and interpupillary distances are periodically refreshed based on individual patient measurements. This periodic variation eliminates fusional locking while maintaining straightforward procedural execution through automated updates.
3Measurement precision
If generic interpupillary distance values are used, then the assessment setup is simple, but measurement precision deteriorates due to neglecting individual differences
Solution Approach 1:
The system applies local quality by customizing the interpupillary distance parameter to match each individual patient's measured value rather than using a generic average. The assessment dynamically adapts the stereogram configuration to the specific patient's anatomical characteristics, ensuring high measurement precision without requiring complex additional equipment beyond standard measurement tools.
Solution Approach 2:
The system performs self-adjustment by automatically setting the interpupillary distance and stereogram parameters based on the patient's measured values. This self-service capability eliminates the need for manual calibration while achieving personalized precision, maintaining operational simplicity through automated parameter configuration.
Data Source
AI summary
Examples of assessing vergence capability of an individual are described. In an example, a first random dot stereogram is generated. The first random dot stereogram includes a first random arrangement of dots and a first sub-portion positioned against the first random dot stereogram. Thereafter, an input from an individual, identifying the position of the first sub-portion against the first random dot stereogram may be received. In response to receiving the input, a second random dot stereogram comprising a second sub-portion positioned against the second random dot stereogram, is generated. The second random dot stereogram comprises a second random arrangement which is different from the first random arrangement of dots in the preceding first random dot stereogram. In response to the second random dot stereogram, another input to identify position of the second sub-portion against the second random dot stereogram is received.