Acne Diagnostic Compute System with Automated Scoring
Find Innovative SolutionsGenerate Solutions
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, color-matched acne marks.
Innovation Solution
A compute system with an acne diagnostic mechanism that detects and segments acne pimples, calculates their area, and generates an objective acne score, utilizing AI and ML models trained with annotated data to provide consistent and accurate scoring, reducing the need for manual annotation by doctors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual acne counting and scoring methods are used by healthcare professionals, then diagnostic process is simple and quick, but measurement precision and reliability are low due to subjectivity and inconsistency
Solution Approach 1:
The patent replaces manual visual inspection and subjective grading by healthcare professionals with an automated image processing system using machine learning models. The system captures skin images, segments acne regions, and calculates objective scores automatically, eliminating human subjectivity while maintaining diagnostic efficiency.
Solution Approach 2:
The patent introduces an intermediate image processing layer between the patient's skin and the final diagnostic score. This intermediary system uses trained machine learning models to objectively quantify acne characteristics, serving as a bridge that translates visual information into reliable numerical scores without direct human intervention.
2Reliability
If automated image processing is used to detect and segment acne, then measurement precision and consistency are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the skin image into distinct regions: normal skin areas and acne-affected areas. The machine learning model processes different regions separately, identifying and isolating individual acne lesions for independent scoring, which improves diagnostic reliability through systematic region-by-region analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-training machine learning models with annotated acne images before deployment. The models are prepared in advance with learned features and patterns, enabling consistent and reliable automated scoring when processing new patient images without requiring real-time complex decision-making.
3Manufacturing precision
If detailed acne segmentation and area calculation are performed, then manufacturing precision of the diagnostic measurement is improved, but loss of time for processing increases
Solution Approach 1:
The patent replaces time-consuming manual tracing and measurement of acne areas with automated image processing algorithms. The machine learning model rapidly segments acne regions and calculates areas pixel-by-pixel, achieving high measurement precision in seconds rather than minutes or hours of manual work.
Solution Approach 2:
The patent maintains continuous useful action by processing images through an optimized pipeline where segmentation, area calculation, and scoring occur in seamless succession. The system processes each pixel and region continuously without interruption, maximizing measurement precision while minimizing total processing time through efficient algorithmic execution.
Data Source
AI summary
A method of operation of a compute system includes: detecting a skin area in a patient image; segmenting the skin area into a segmented image having an acne pimple at the center; generating a target pixel array from the segmented image includes separating a plurality of the acne pimples that are adjacent in the segmented image; identifying an acne characterization of the acne pimples including an area of each acne and an acne score; and assembling a user interface display from the acne characterization for displaying on a device.