Machine Learning Cosmetic Injector Zone Detection
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Solution Overview
Problem
Cosmetic treatments, such as facial injectables, face challenges in accurately identifying injectable zones and determining the appropriate types and quantities of injectables, leading to potential complications due to human error, and lack effective visualization tools for both patients and professionals.
Innovation Solution
A system and method utilizing a machine learning-based recommendation system that detects injectable zones, determines aesthetic scores, and generates visual recommendations for modifying these zones to achieve a predefined aesthetic threshold, incorporating a processor with a machine learning module to analyze images and provide precise location coordinates for injectable placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional verbal explanation methods are used to explain treatments to patients, then the treatment process remains simple and quick, but the patient's understanding and visualization of the treatment outcome is insufficient
Solution Approach 1:
The system creates a digital copy of the patient's body region using image processing and machine learning to generate a virtual model. This copy allows for visualization and simulation of treatment outcomes without affecting the actual patient, enabling patients to see potential results before proceeding with the actual treatment.
Solution Approach 2:
The patent replaces the mechanical/verbal explanation method with an automated computer-based system using machine learning algorithms and image processing. This substitution enables automatic detection of injectable zones, calculation of aesthetic scores, and generation of treatment recommendations without requiring complex manual analysis.
2Loss of information
If AR-based visualization applications are used to show treatment outcomes, then patients can visualize potential results, but these applications cannot be replicated in real life and provide no assistance to professionals in performing the treatment
Solution Approach 1:
The system provides feedback by comparing the detected injectable zones and aesthetic scores against established medical guidelines and aesthetic standards. The machine learning model learns from training data and continuously improves its recommendations, providing feedback loops that enhance both visualization accuracy and treatment reliability.
Solution Approach 2:
The system serves multiple functions: it visualizes treatment outcomes for patients, provides detailed treatment plans for professionals, detects injectable zones automatically, calculates aesthetic scores, and generates recommendations. This multi-functional approach replaces the need for separate AR visualization tools and professional analysis tools.
3Measurement precision
If manual identification of injectable zones is performed by professionals, then the treatment can be customized, but human error increases and consistency across different practitioners is reduced
Solution Approach 1:
The system enables self-service by allowing the computerized system to automatically detect injectable zones, calculate aesthetic scores, and generate treatment recommendations without requiring manual intervention for these specific tasks. The machine learning model performs these functions autonomously based on input images, reducing dependency on individual practitioner expertise while maintaining consistency.
4Reliability
If extensive training and experience are required for professionals to perform cosmetic treatments safely, then treatment quality can be maintained, but the barrier to entry increases and more time is needed for professional development
Solution Approach 1:
The system acts as an intermediary between the practitioner and the treatment execution. The machine learning-based recommendation system provides automated guidance, zone detection, and treatment planning that supplements practitioner knowledge. This intermediary layer enhances safety by providing consistent, data-driven recommendations while reducing the burden on individual practitioners to have extensive memorized knowledge of all anatomical variations and treatment parameters.
Data Source
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AI summary
The present disclosure provides a system and method for recommending injectables for cosmetic treatments. An input image including a body regions such as face, is received. The system uses a machine learning module to detect one or more injectable zones within the body region. The system determines an aesthetic score of the body region based on the injectable zones and identifies one or more injectable zones that can be modified by injecting injectables to achieve an augmented body region that has a revised aesthetic score satisfying a predefined threshold. The system then generates an output recommendation image to be displayed on an output device. The output recommendation image indicates the system identified one or more injectable zones that can be modified.