Skin Image Analysis with Environmental Distortion Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately analyze images of a person's skin for medical conditions like vitiligo due to distortions caused by environmental and non-environmental factors, leading to inaccurate determinations of the presence and severity of the condition.
Innovation Solution
A system that preprocesses images by generating optimized vector representations, accounting for distortions from environmental and non-environmental factors, using machine learning models to analyze skin pigmentation and generate accurate determinations of medical conditions, including vitiligo, and tracks changes in severity over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed without accounting for environmental and non-environmental factors, then processing speed is maintained, but measurement precision deteriorates due to image distortions
Solution Approach 1:
The system performs preliminary actions by capturing environmental data (lighting conditions, temperature, humidity) and non-environmental data (camera settings, device information) before image analysis. This pre-captured data is then used to compensate for distortions during the skin condition detection process, thereby improving measurement precision without requiring complex real-time processing adjustments
Solution Approach 2:
Environmental and non-environmental data serve as intermediary elements that mediate between the raw image and the final analysis result. These intermediary data points are used to adjust and compensate for distortions, acting as a bridge that improves detection accuracy without directly modifying the core image processing algorithm
2Measurement precision
If environmental and non-environmental factors are accounted for in image processing, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system implements multi-functionality by using a single integrated approach that simultaneously captures environmental data, non-environmental data, and image data. This unified system performs multiple functions (environmental monitoring, device parameter tracking, image capture) through coordinated components, improving precision while managing complexity through functional integration rather than separate dedicated systems for each data type
3Productivity
If continuous image monitoring is implemented, then productivity of medical condition tracking improves, but loss of time for data processing increases
Solution Approach 1:
The system applies local quality by focusing processing efforts on specific critical features of the skin condition rather than analyzing entire images uniformly. By identifying and prioritizing key regions and characteristics that indicate condition severity, the system can process images more efficiently, enabling continuous monitoring while reducing overall processing time through targeted rather than comprehensive analysis
Data Source
AI summary
Methods, systems, and computer programs for monitoring skin condition of a person. In one aspect, a method can include obtaining data representing a first image, the first image depicting skin from at least a portion of a body of a person, generating a severity score that indicates a likelihood that the person is trending towards an increased severity of an auto-immune condition or trending towards a decreased severity of an auto-immune condition, comparing, the severity score to a historical severity score, wherein the historical severity score is indicative of a likelihood that a historical image of the user depicts skin of a person having the auto-immune condition, and determining based on the comparison, whether the person is trending towards an increased severity of the auto-immune condition or trending towards a decreased severity of the auto-immune condition.