AI Visualization for Aesthetic Procedures
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Solution Overview
Problem
Current aesthetic medical procedures lack effective visualization tools for patients to understand expected outcomes, relying on stock images and subjective editing, which fail to accurately represent individual results, leading to dissatisfaction due to poor imaging and lack of personalized visualization.
Innovation Solution
An AI-enhanced visualization system using machine learning models, such as neural networks, to predict aesthetic outcomes by analyzing before and after images, allowing patients to see predicted results in augmented reality, and enabling clinicians to plan and explain treatments more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If stock before and after images are used for visualization, then the explanation of procedures is simplified, but the accuracy and personalization of outcome prediction deteriorates
Solution Approach 1:
The system creates a digital copy of the patient's face using a generative adversarial network (GAN) model. This digital twin allows for accurate, personalized manipulation and visualization of potential treatment outcomes without requiring physical alterations or relying on stock images of other patients.
Solution Approach 2:
The system manipulates parameters of the generated face image to simulate different treatment outcomes. By adjusting specific facial parameters in the digital copy, the system can show patients personalized predictions of their appearance after various aesthetic procedures, maintaining both accuracy and ease of explanation.
2Productivity
If simple photography and verbal explanation are used during patient intake, then the process is efficient and quick, but patient understanding and satisfaction with expected outcomes deteriorates
Solution Approach 1:
The system performs preliminary visualization of treatment outcomes during the patient intake process itself, rather than waiting until after treatment planning. By generating and displaying personalized predicted outcome images early in the consultation, patients gain immediate visual understanding of expected results, improving their comprehension without significantly extending the intake time.
3Device complexity
If subjective image editing is used to show before and after results, then the visualization process is simple, but the reliability and accuracy of outcome representation deteriorates
Solution Approach 1:
The system replaces manual, subjective image editing processes with an automated artificial intelligence system. The GAN-based model objectively generates personalized predictions based on learned patterns from training data, eliminating the subjectivity and inconsistency inherent in manual photo manipulation while maintaining operational simplicity for clinicians.
Data Source
AI summary
A method includes obtaining, by a processor, an image of a patient using an imaging device, presenting the image of the patient on a display, and selecting one or more medical procedures to apply to the patient. The method further includes generating a modified image of the patient by applying the one or more medical procedures and the image of the patient as input to a machine learning model trained to output the modified image of the patient. The modified image of the patient includes one or more body region representations of the patient that are modified due to application of the one or more medical procedures to the image of the patient. The method also includes presenting the modified image of the patient on the display.