Dental Shade Matching via ML Server and Intermediary Architecture
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Solution Overview
Problem
Current CAD/CAM dentistry systems lack a predictable and biomimetic esthetic outcome for dental restorations, as they rely on manual shade matching and lack integration with machine learning, supply management, and efficient delivery platforms, leading to suboptimal use of CAD/CAM machines and variability in restoration quality.
Innovation Solution
A system integrating CAD/CAM dentistry with machine learning, supply management, shade matching technologies, and delivery platforms to recommend and deliver dental restorations based on geographic area, material availability, and patient-specific shade measurements, using a server device that processes stereo lithic files and shade measurements to provide material and characterization recommendations, and facilitates the manufacturing, delivery, and staining/glazing of restorations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual shade matching methods are used in CAD/CAM dentistry, then the system is simple to operate, but the esthetic outcome precision and reliability are insufficient
Solution Approach 1:
A server device acts as an intermediary between multiple client devices (dental practices) and the centralized machine learning model. The server receives shade measurements and stereo lithic files from clients, processes them through the ML model, and returns material and characterization recommendations. This intermediary architecture enables access to sophisticated shade matching capabilities without requiring each individual practice to implement complex ML infrastructure locally.
Solution Approach 2:
The patent replaces manual mechanical shade matching processes with an automated machine learning system. Instead of relying on dentist expertise and manual comparison of shade guides, the system uses spectral data from spectrophotometers processed through ML algorithms to automatically determine optimal restoration materials and characterization parameters, significantly improving measurement precision and consistency.
2Reliability
If a centralized machine learning system is implemented across multiple dental practices, then the esthetic outcome and shade matching accuracy improve, but the device complexity and infrastructure requirements increase
Solution Approach 1:
The server device provides universal access to the machine learning model for multiple dental practices simultaneously. A single centralized system serves multiple clients, enabling each practice to benefit from sophisticated shade matching and material recommendation capabilities without individually implementing complex infrastructure. The system handles multiple client requests, manages inventory data from various sources, and provides consistent recommendations across the network.
Solution Approach 2:
The server acts as a mediator that simplifies the interaction between multiple client devices and the complex machine learning infrastructure. It handles data aggregation, model invocation, result processing, and recommendation generation, shielding individual practices from the underlying system complexity while maintaining high reliability and consistency in restoration quality.
3Adaptability or versatility
If extensive inventory management across multiple practices is integrated, then material availability and recommendation accuracy improve, but the loss of time for data processing and coordination increases
Solution Approach 1:
The system performs preliminary actions by pre-aggregating inventory data from multiple practices and pre-training the machine learning model with extensive material and characterization data. When a client submits a request, the model can quickly query against pre-processed inventory information and generate recommendations without requiring real-time data collection from all sources, significantly reducing processing time while maintaining comprehensive material availability.
Solution Approach 2:
The patent replaces manual inventory coordination and material selection processes with automated machine learning-based recommendation systems. The ML model automatically queries inventory databases, compares material properties against shade measurements and design requirements, and generates optimized recommendations, eliminating the need for manual inventory management and significantly accelerating the material selection process.
4Manufacturing precision
If machine learning models process extensive stereo lithic files and shade measurements, then the characterization recommendations accuracy improves, but the computational energy consumption and processing time increase
Solution Approach 1:
The system segments the computational workload by dividing the machine learning processing into discrete model invocations for each client request. The server processes stereo lithic files and shade measurements in manageable units, invoking the ML model only when needed rather than continuously processing all data. This segmented approach reduces overall energy consumption while maintaining high manufacturing precision through targeted, on-demand analysis.
Solution Approach 2:
The patent replaces energy-intensive manual material characterization and selection processes with efficient machine learning computations. The ML model analyzes spectral data and design parameters using optimized algorithms that require significantly less computational energy compared to traditional methods of manual material testing and characterization, achieving high precision recommendations with reduced energy consumption.
Data Source
AI summary
Embodiments of the present disclosure create an “exchange” where dentists who invest in on-site restoration technologies can share their restoration design, restoration supplies, characterization knowledge, and idle milling time with local dentists. Suppliers can further offer quality control by double checking designs and selections. The platform of shade measurements, block selections, and characterization recommendations further serves as a resource to the dental community at large for improving shade matching in dentistry restorations.