AI Scalp Diagnostic System Using Deep Learning Analysis
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Solution Overview
Problem
Conventional scalp diagnostic systems are slow and inaccurate, requiring manual comparison of images and relying on expert analysis, with limited ability to diagnose specific scalp types and provide improving methods based on severity.
Innovation Solution
A system that transmits scalp questionnaire data and images to a server for AI analysis, using a self-diagnosis algorithm and deep learning to classify scalp types and recommend improving methods and products based on diagnosed conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image comparison by diagnosticians is used, then diagnostic accuracy can be maintained through expert analysis, but diagnostic speed becomes slow and productivity is low
Solution Approach 1:
The patent replaces the mechanical system of manual image comparison by diagnosticians with an automated AI-based image analysis system. The AI processor automatically analyzes scalp images to diagnose scalp types, replacing the need for manual expert analysis while maintaining diagnostic accuracy and significantly improving diagnostic speed.
Solution Approach 2:
The system enables self-service diagnosis where the AI processor independently analyzes scalp images and questionnaire data without requiring continuous human intervention. The diagnostician only needs to input basic information, and the system automatically completes the diagnosis, reducing manual workload while maintaining accuracy.
2Measurement precision
If comprehensive scalp analysis is performed manually, then diagnostic accuracy can be improved, but the complexity of the diagnostic process increases
Solution Approach 1:
The patent segments the diagnostic process into distinct functional modules: a data input module for questionnaire collection, an AI processor for image and data analysis, and a result output module. This segmentation allows comprehensive scalp analysis to be performed systematically while reducing overall process complexity through modular design.
Solution Approach 2:
The AI processor acts as an intermediary between the input questionnaire data/images and the final diagnosis results. It automatically processes and integrates multiple data sources (scalp images, questionnaire responses) to produce comprehensive analysis, reducing the complexity burden on diagnosticians.
3Measurement precision
If AI analysis is performed on all scalp images through main processor, then diagnostic accuracy is improved, but server load increases and analysis speed decreases
Solution Approach 1:
The patent applies partial action by having the AI processor analyze only specific scalp images and data that require detailed examination, rather than processing all images uniformly. The system selectively applies AI analysis where needed, improving diagnostic accuracy for critical cases while maintaining faster processing for routine cases, thus balancing accuracy and speed.
4Measurement precision
If expert analysis is required for each diagnosis case, then diagnostic accuracy is maintained, but the ease of operation becomes difficult and time consumption increases
Solution Approach 1:
The system enables self-service diagnosis where users can independently complete scalp assessments through the questionnaire and image upload interface. The AI processor automatically analyzes the data and provides diagnosis results without requiring expert intervention for each case, making the system easy to operate while maintaining reliability through automated analysis.
Solution Approach 2:
The system provides automated feedback through the AI processor that analyzes uploaded images and questionnaire data, then returns diagnosis results and recommendations. This feedback mechanism maintains diagnostic reliability by systematically evaluating all input data while improving ease of operation by eliminating the need for users to manually seek expert analysis.
Data Source
AI summary
The present invention relates to a scalp type diagnostic system on the basis of scalp state information, and a scalp improving method using same, the system transmitting measurements of scalp questionnaire data and a scalp image to a server, storing same, and sharing the stored scalp questionnaire data and scalp image with a recommended service server and an artificial intelligence server to diagnose and analyze same, thereby accurately analyzing scalp state information with maximized speed and efficiency, diagnosing a scalp type on the basis of the analysis, carrying out a scalp improving method in accordance with the diagnosed scalp type, and recommending a suitable product in accordance with the scalp type.