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AI-powered service for creating ideal dental occlusion based on a client's photograph
Customer
Dental clinic
goal
To develop an automated AI-powered service that allows patients to visualize the potential outcome of orthodontic treatment, receiving a processed image with an improved bite and an aesthetically pleasing smile.
The service is designed to be integrated into the doctor's personal account and increase patient engagement.
The service is designed to be integrated into the doctor's personal account and increase patient engagement.
CHALLENGES/FEATURES
High demands for realism:
The processing result must look as natural as possible to inspire confidence in the patient.
Diversity of input data:
The quality and angle of photographs provided by users can vary significantly.
Computing resources:
Significant computing power (GPU) is required to process images in an acceptable timeframe.
Processing queue:
Implementation of a request queue to ensure stable processing of concurrent requests.
The processing result must look as natural as possible to inspire confidence in the patient.
Diversity of input data:
The quality and angle of photographs provided by users can vary significantly.
Computing resources:
Significant computing power (GPU) is required to process images in an acceptable timeframe.
Processing queue:
Implementation of a request queue to ensure stable processing of concurrent requests.
Develop a REST API service capable of accepting a facial image as input, detecting dental areas, correcting the bite, and generating an image with an improved smile.
The service must be scalable, reliable, and integrate with the existing infrastructure of the doctor's personal account.
The service must be scalable, reliable, and integrate with the existing infrastructure of the doctor's personal account.
Task
Technical solution
The service was deployed on a dedicated GPU server, ensuring high performance and reliability.
Full technical documentation (API description) and the service source code were provided.
The service was tested and integrated into the doctor's personal account.
Architecture:
Microservice architecture, separated into a backend service (receiving and processing requests)
and an AI processing service (executing the generation pipeline).
Programming Language:
Python
Frameworks and Libraries:
ComfyUI / LoRA, OpenCV computer vision libraries.
Infrastructure:
GPU server (RTX 3090) for hosting and executing the AI processing service.
API:
REST API using the JSON format for data exchange.
Data Storage:
Temporary storage of original and final images on a local drive with automatic deletion after processing.
Automation:
Using Docker to containerize the application and simplify deployment and scaling.
Full technical documentation (API description) and the service source code were provided.
The service was tested and integrated into the doctor's personal account.
Architecture:
Microservice architecture, separated into a backend service (receiving and processing requests)
and an AI processing service (executing the generation pipeline).
Programming Language:
Python
Frameworks and Libraries:
ComfyUI / LoRA, OpenCV computer vision libraries.
Infrastructure:
GPU server (RTX 3090) for hosting and executing the AI processing service.
API:
REST API using the JSON format for data exchange.
Data Storage:
Temporary storage of original and final images on a local drive with automatic deletion after processing.
Automation:
Using Docker to containerize the application and simplify deployment and scaling.
Business values
For orthodontists:
For patients:
Increased patient engagement:
Visualizing the expected treatment outcome motivates patients to begin and continue treatment.
Improved communication with patients:
The service allows for a more visual explanation of the treatment plan and expected results.
Increased treatment consent:
Visualizing a positive outcome increases patient trust and the likelihood of treatment consent.
Time savings:
Automating the visualization step saves physician time and allows them to focus on more complex tasks.
Visualizing the expected treatment outcome motivates patients to begin and continue treatment.
Improved communication with patients:
The service allows for a more visual explanation of the treatment plan and expected results.
Increased treatment consent:
Visualizing a positive outcome increases patient trust and the likelihood of treatment consent.
Time savings:
Automating the visualization step saves physician time and allows them to focus on more complex tasks.
Realistic vision of the outcome:
Being able to see how your smile will change after treatment helps you make an informed decision.
Reduced anxiety:
Understanding the treatment process and expected results reduces anxiety and fear about orthodontic treatment.
Convenience and accessibility:
Visualizations are available remotely, without visiting the clinic.
Being able to see how your smile will change after treatment helps you make an informed decision.
Reduced anxiety:
Understanding the treatment process and expected results reduces anxiety and fear about orthodontic treatment.
Convenience and accessibility:
Visualizations are available remotely, without visiting the clinic.
Results of implementation
The service is a REST API that accepts a user's photo as input and returns a processed image visualizing the orthodontic treatment results.
The processing pipeline includes the following steps:
Image acquisition:
The received image is assigned a unique ID, after which it is sent to the processing queue, and its status is changed to "in processing."
Face and smile detection:
Computer vision algorithms are used to detect faces and smiles in the image.
Bite correction and new smile generation:
Diffusion neural networks are used to correct bite and create an aesthetically pleasing smile.
Final image generation:
After processing, the status is changed to "ready," and the final image becomes available for retrieval.
Result regeneration:
A method is provided for regenerating the result.
Delete original files:
The original files are automatically deleted from the server after processing is complete and the result is presented to the user.
The processing pipeline includes the following steps:
Image acquisition:
The received image is assigned a unique ID, after which it is sent to the processing queue, and its status is changed to "in processing."
Face and smile detection:
Computer vision algorithms are used to detect faces and smiles in the image.
Bite correction and new smile generation:
Diffusion neural networks are used to correct bite and create an aesthetically pleasing smile.
Final image generation:
After processing, the status is changed to "ready," and the final image becomes available for retrieval.
Result regeneration:
A method is provided for regenerating the result.
Delete original files:
The original files are automatically deleted from the server after processing is complete and the result is presented to the user.
By clicking the "Submit" button, you expressly consent to the processing of your personal data to the extent and for the purposes defined in the Personal Data Processing Policy.
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