Machine Learning Architecture for Dental Image Quality Feedback
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Solution Overview
Problem
Users face difficulties in capturing high-quality images of their mouths due to lack of access to suitable hardware and the inconvenience, cost, and inefficiency of seeking professional assistance.
Innovation Solution
A system utilizing a machine learning architecture that includes a capture device, communication device, and processor to receive and analyze images, providing user feedback to improve image quality by guiding users through capturing high-quality images based on image quality scores and user-specific preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users capture images of their mouth using available hardware, then image capture becomes accessible and convenient, but image quality is insufficient
Solution Approach 1:
The system provides real-time feedback to users about image quality metrics (brightness, focus, angle, completeness) and guides them to adjust their capture technique. This feedback loop enables users to achieve high-quality images using standard hardware by iteratively improving their capture approach based on system guidance.
Solution Approach 2:
The system performs preliminary analysis of captured images to assess quality metrics before final processing. By evaluating images against predefined quality thresholds and providing immediate feedback, the system guides users to make necessary adjustments before submitting final images, ensuring high quality without requiring expensive hardware.
2Manufacturing precision
If users seek professional assistance for capturing mouth images, then image quality improves, but time consumption and cost increase
Solution Approach 1:
The system enables users to independently capture and evaluate their own mouth images using standard devices. Through automated quality assessment and guidance, users can perform what previously required professional assistance, eliminating travel time and appointment scheduling while maintaining image quality through algorithmic evaluation and feedback.
Solution Approach 2:
The patent replaces the mechanical system of professional physical examination with an automated digital image analysis system. Machine learning algorithms and quality metrics substitute for professional expertise, allowing users to capture and evaluate images independently, thereby eliminating the time loss associated with visiting professionals while maintaining diagnostic quality.
3Manufacturing precision
If users seek professional assistance for capturing mouth images, then image quality improves, but cost increases
Solution Approach 1:
The system empowers users to perform self-assessment and self-correction of image quality issues using free or low-cost standard devices. By providing automated guidance and evaluation, the system replaces expensive professional services with accessible technology, maintaining image quality while eliminating associated costs.
Solution Approach 2:
The system utilizes standard, widely available camera devices (smartphone cameras, webcams) that users already possess, replacing the need for expensive specialized imaging equipment. These common devices, when used with the system's guidance algorithms, achieve sufficient image quality without requiring costly hardware investments or professional service fees.
4Manufacturing precision
If the system provides detailed feedback to users for improving images, then image quality improves, but computational resources increase
Solution Approach 1:
The system implements a tiered feedback approach that provides detailed guidance only when image quality thresholds are not met. For images that already satisfy quality requirements, minimal or no feedback is provided, reducing computational overhead. The system performs selective analysis focusing only on deficient aspects of substandard images rather than comprehensively evaluating all images.
Solution Approach 2:
The feedback system is divided into multiple independent evaluation modules (brightness assessment, focus detection, angle verification, completeness checking). Each module independently evaluates specific image attributes and provides targeted feedback only for deficient aspects, reducing overall computational burden compared to a monolithic analysis system while maintaining comprehensive quality assessment.
Data Source
AI summary
Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.