AI-Powered Anatomical Image Matchmaking Platform – Transforming Plastic Surgery Consultations
The AI-Based Image Matchmaking Application is a Proof of Concept (POC) designed to revolutionise the initial consultation process for plastic surgery businesses. This centralised digital ecosystem allows users to upload or scan photos of their own anatomy and receive matches from a database of real before-and-after results. By utilising advanced AI and image recognition, the platform provides personalised and realistic outcomes for prospective patients. The system focuses on key anatomical categories and uses a multi-view upload process to ensure accuracy in results, bridging the gap between patient expectations and surgical possibilities.
The Client
Name: Gaincafe Technologies
The Challenge
- Subjective Outcome Expectations
Patients often struggle to visualise how a procedure might look on their specific body type, leading to unrealistic expectations. - Complex Anatomical Matching
Matching a user’s unique anatomy to a specific "before" case requires precise image alignment across multiple angles. - Data Accuracy Requirements
For the matchmaking to be effective, images must be captured in standardised views
(Facing front, 45-degree turns, and side profiles) to minimise "noise" from clothing or lighting. - POC Scalability
The initial phase required a lean approach focusing on four primary categories (Face, Breast, Abdomen, Arms/Legs) while maintaining a path for future expansion.
The Solution
- Intelligent Image Matchmaking Engine
Developed a feature that matches user-uploaded anatomy scans with a curated database of 300-400 real medical cases to display the most resembling "Before/After" result. - Standardised View Capture System
Implemented a guided camera interface requiring five specific angles (Facing right, Facing front, Facing left) to ensure high-fidelity scanning. - Category-Specific Instruction Modules
Created dedicated detail pages for Face, Breast, Arms/Legs, and Abdomen, providing users with photographic standards, target area framing, and positioning guides. - Static Informational Hub
Developed static templates for "About Us," "Privacy Policy," and "Terms & Conditions" to provide a professional brand presence during the POC phase. - AI-Integrated Backend
Leveraged Azure/AWS image recognition to process anatomical data and return relevant matches via a Fast API framework.
The Results
The implementation of the AI Image Matchmaking POC successfully demonstrated the feasibility of automated anatomical comparisons. Key outcomes include:
- Personalised User Experience
Users receive a single, highly relevant "before and after" result tailored to their specific anatomy. - Standardised Medical Photography
Established a "Photographic Standards Chart" to guide users in capturing clinical-grade images at home. - Optimised Resource Engagement
Demonstrated feasibility within a compact 30-day development window using a dedicated AI Engineer and Mobile Developer. - High-Accuracy Potential
Targeted matching across four core categories, reducing manual sorting and increasing consultation efficiency. - Foundation for Scalability
Developed a scalable framework capable of supporting more images and anatomical categories beyond the initial MVP scope.
The POC successfully validates the use of AI to bridge the gap between patient expectations and surgical reality. By providing personalised, data-driven "before and after" matches, the application enhances the user experience and streamlines the initial consultation process. This project establishes a scalable foundation for future AI integration in the plastic surgery industry.
