Automated Skin Lesion Scoring via Digital Image Analysis
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Solution Overview
Problem
Current methods for evaluating acne severity and assessing the efficacy of treatments in clinical trials are subjective and time-consuming, relying heavily on dermatological experience and requiring repetitive patient visits, which can be inefficient and prone to subjective interpretation.
Innovation Solution
The implementation of computer-implemented systems and methods that analyze digital images using entropy-based filtering, thresholding, discrete wavelet frames, and gray-level co-occurrence matrices to automatically quantify and classify skin lesions, enabling objective assessment of acne and other skin diseases like rosacea.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dermatologists manually count and document each lesion type, then accurate acne severity evaluation can be achieved, but it requires extensive time and is difficult to complete during limited consultation time
Solution Approach 1:
The patent replaces the mechanical manual counting and documentation process with an automated digital image analysis system. The system captures images of skin lesions and uses computer algorithms to automatically detect, classify, and count different lesion types (comedones, papules, pustules, cysts, and scars), eliminating the need for manual inspection and documentation while maintaining high measurement accuracy.
Solution Approach 2:
The patent creates a digital copy of the skin lesions through photography, allowing the lesions to be analyzed in a virtual environment rather than requiring direct manual examination. This digital replica can be processed by automated algorithms to count and classify lesions, providing accurate measurement without consuming clinical consultation time.
2Reliability
If multiple repetitive patient visits are required to determine treatment efficacy, then comprehensive assessment can be achieved, but it increases time consumption and patient burden
Solution Approach 1:
The patent enables continuous monitoring of skin lesions through automated image analysis at multiple time points. By capturing and analyzing images at baseline and during treatment follow-up, the system continuously tracks lesion changes without requiring frequent in-person visits. This maintains reliable treatment efficacy assessment while reducing patient time commitment.
Solution Approach 2:
The patent introduces digital imaging and automated analysis as an intermediary between patient visits. Instead of requiring direct dermatologist-patient interaction for each assessment, the digital image analysis system serves as a mediator that objectively measures lesion changes, providing reliable efficacy data with minimal patient involvement.
3Measurement precision
If manual lesion counting is performed, then subjective determination by medical personnel can be avoided, but the process becomes extremely time-consuming and complex
Solution Approach 1:
The patent replaces complex manual lesion identification and counting with automated computer vision algorithms. The system uses digital image processing to detect, segment, and classify different lesion types based on their visual characteristics, providing accurate counts without requiring complex manual procedures or extensive dermatologist time.
Solution Approach 2:
The patent enables the assessment system to perform its own analysis automatically. The digital image analysis system independently identifies and counts lesions without requiring manual intervention for each lesion type, making the complex process self-executing and eliminating the need for time-consuming manual documentation.
Data Source
AI summary
Disclosed are systems and methods for clinical trial assessment of skin disease treatment. The disclosure includes obtaining a series of digital images over a period of time, wherein each digital image includes an affected area of the subject; identifying characteristic morphologies and lesions in the affected area of the subject in each of the digital images; classifying each of the detected and segmented morphologies and lesions into one or more identified categories for each of the digital images; assigning a global score to each of the digital images based on a count of the detected and segmented characteristic morphologies and lesions in each of the one or more identified categories; analyzing the global scores of each of the digital images; and making an assessment of the clinical trial based on the analysis of the global scores of each of the digital images.