Acne Diagnostic Compute System with Automated Scoring
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Solution Overview
Problem
Current methods for acne diagnosis are subjective and inconsistent, often leading to missed acne locations and inaccurate scoring, which can result in inadequate treatment and long-lasting scars, especially in adolescents, due to the challenges of counting small and color-matched acne marks.
Innovation Solution
A compute system with an acne diagnostic mechanism that detects and analyzes skin areas in patient images, generates normalized images, crops target pixel arrays to calculate acne severity scores, and displays acne types and indications, utilizing AI and ML models trained with annotated data to provide objective and consistent scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual doctor annotation is used for acne diagnosis, then flexibility and adaptability are maintained, but measurement precision and consistency deteriorate due to subjective scoring
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated image processing system that uses algorithms to detect, segment, and score acne lesions. The system substitutes human visual inspection and manual marking with computational image analysis, thereby eliminating subjectivity while maintaining diagnostic capability.
Solution Approach 2:
The system enables self-service by allowing the acne diagnosis to be performed automatically without requiring doctor annotation. The algorithm independently detects acne lesions, calculates severity scores, and generates diagnostic reports, making the system self-sufficient in the annotation task.
2Productivity
If manual annotation methods are used, then ease of operation is maintained, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The manual annotation process is replaced with automated image processing algorithms that can analyze multiple images simultaneously. The system performs acne detection, segmentation, and scoring through computational methods, dramatically reducing the time required per diagnosis while maintaining or improving accuracy.
Solution Approach 2:
The system performs preliminary automated annotation before clinical review, pre-processing the images by detecting and marking acne lesions. This preliminary action reduces the subsequent time needed for manual verification and speeds up the overall diagnostic workflow.
3Measurement precision
If simple counting methods are used, then ease of operation is improved, but measurement precision deteriorates due to missed acne locations
Solution Approach 1:
The patent applies segmentation by dividing the skin image into multiple regions and using algorithms to identify and mark individual acne lesions within each region. This segmented approach ensures that no acne is missed, as the system systematically processes the entire image area rather than relying on holistic visual assessment.
Solution Approach 2:
The system introduces an intermediary computational layer between the raw image and the final diagnosis. This intermediary processing stage includes automated detection algorithms that act as a mediator to identify all acne lesions, ensuring comprehensive detection while maintaining operational simplicity through automated workflows.
Data Source
AI summary
A method of operation of a compute system comprising: detecting a skin area in a patient image; generating a normalized image by applying a rescale factor to the skin area; cropping a target pixel array from the normalized image with an acne pimple at the center of the target pixel array; generating an acne type by analyzing the target pixel array including calculating an acne severity score and calculating an area of each acne; and generating a user interface display including an acne indication and the acne severity score for displaying on a device.