CASE STUDY
AI-Generated Dental Simulation
TechnoLynx began working with Tasty Tech on the second-generation product. The goal was to develop a second-generation version that would be more automated, easier to use, and still allow for manual adjustments when necessary.
The Challenge
Our client, Tasty Tech, was an up-and-coming start-up in the dental industry. Their first-generation product had already found a good fit in the market, but it required too much manual tuning from users. The client wanted to develop a second-generation version that would be more automated, easier to use, and still allow for manual adjustments when necessary.
Too much manual tuning
Dental professionals had to spend more time than desired fine-tuning the results, which could be cumbersome and time-consuming.
Automate without losing control
The client wanted to develop a second-generation version that would be more automated, easier to use, and still allow for manual adjustments when necessary.
End-to-end quality and usability
Early in the project, we achieved satisfactory results, which allowed us to expand the scope of our work. Instead of focusing solely on synthesis, we decided to enhance the entire image processing pipeline, including segmentation, classification, and structural deformation.
Dental Simulation - Example 1
Project Timeline
From an agreed state-of-the-art model to an end-to-end pipeline and the launch of “Dentrino On-Demand”
Baseline Alignment
TechnoLynx began working with Tasty Tech on the second-generation product by focusing on a state-of-the-art model that both parties agreed upon. Our primary goal was to automate as much of the process as possible while maintaining the flexibility needed for manual editing.
Modified the model carefully to meet functional requirements for synthesis while maintaining quality.
Model Adaptation
Pipeline Expansion
Early in the project, we achieved satisfactory results, which allowed us to expand the scope of our work. Instead of focusing solely on synthesis, we decided to enhance the entire image processing pipeline. This included not just synthesis but also segmentation, classification, and structural deformation.
Our team executed a series of experiments, drawing on both established methods and novel concepts that had not been previously applied in this context. By continuously refining the pipeline, we could deliver gradual improvements on an on-demand basis.
Iterative R&D
Delivery & Launch
Delivered an end-to-end system and supported protection of novel IP created during the project, culminating in the launch of the second-generation product.
The Solution
We began by building on a state-of-the-art model, prioritising automation while preserving manual editability. After early results validated the approach, we expanded to improve the complete image processing pipeline and delivered iterative improvements through structured experimentation.
State-of-the-art Foundation
Started from an agreed state-of-the-art model and adapted it carefully to satisfy synthesis requirements without compromising output quality.
End-to-End Pipeline
Expanded from synthesis into a full image pipeline upgrade, including segmentation, classification, and structural deformation to support a comprehensive second-generation product.
Iterative Improvement Model
Our team executed a series of experiments, drawing on both established methods and novel concepts that had not been previously applied in this context. By continuously refining the pipeline, we could deliver gradual improvements on an on-demand basis.
Technical Specifications
Framework
PyTorch (particularly well-suited for projects involving significant research and experimentation)
Scope
Synthesis, segmentation, classification, and structural deformation
PyTorch was an excellent choice for the research phase. During the delivery phase, TensorFlow might have offered more optimised solutions. However, the flexibility of PyTorch allowed us to explore new ideas and test various approaches, leading to continuous improvements in the project.
The Outcome
The result of our collaboration with Tasty Tech was a fully functional, end-to-end pipeline that greatly enhanced their dental simulation product. This new system featured a clear yet flexible roadmap, allowing for ongoing improvements based on the latest research and innovative ideas. The AI-generated images produced by the system were of high quality, vibrant, and realistic, and the AI image generator produces high-quality images based on text prompts. The project culminated in the successful launch of “Dentrino On-Demand,” offering dental practitioners a powerful AI-generated smile makeover simulation designed to help dental professionals create realistic and appealing simulations of potential dental treatments for their patients.
Key Achievements
Our primary goal was to automate as much of the process as possible while maintaining the flexibility needed for manual editing.
Expanded scope from synthesis to an end-to-end pipeline including segmentation, classification, and structural deformation
The AI-generated images produced by the system were of high quality, vibrant, and realistic, making them ideal for use in various settings, from marketing materials to practical applications in dental practices.
Established an iterative improvement model that enabled gradual, on-demand enhancements
Supported protection of novel IP created during the R&D work
The project culminated in the successful launch of “Dentrino On-Demand,” the second-generation product that had graduated from its beta phase.
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