Dental Image Data Mining with ML Anatomy and Pathology Identification
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Solution Overview
Problem
Current systems for dental image data mining do not effectively identify anatomy, pathology, and treatment associated with dental images, nor do they provide correlated information for diagnostic aids.
Innovation Solution
A system utilizing an aggregator server with machine learning (ML) anatomy and pathology/treatment datasets to process dental images, identify anatomy and pathology, and integrate this information into patient datasets for cluster analysis, providing correlated dental image information for diagnostic aids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dental image analysis methods are used, then manual processing is simple, but identification accuracy of anatomy and pathology is low
Solution Approach 1:
The patent replaces manual mechanical analysis with machine learning-based automated analysis. The ML model processes dental images to identify anatomy and pathology, substituting human expert analysis with algorithmic processing that achieves higher accuracy and consistency while reducing manual intervention.
Solution Approach 2:
The patent introduces an aggregator server as an intermediary component that coordinates between multiple specialized services (ML anatomy identification, ML pathology identification, treatment recommendation). This intermediary manages the complexity by orchestrating specialized modules rather than requiring a single monolithic complex system.
2Loss of information
If manual dental image analysis is performed, then system complexity is low, but diagnostic information completeness is insufficient
Solution Approach 1:
The patent segments the dental image analysis task into distinct specialized modules: anatomy identification service, pathology identification service, and treatment recommendation service. Each module focuses on a specific aspect of diagnosis, ensuring comprehensive information extraction while allowing independent optimization of each component.
Solution Approach 2:
The aggregator server provides universal functionality by serving multiple purposes: coordinating ML model inference, managing patient data storage, facilitating cluster analysis, and providing treatment recommendations. This multi-functional approach consolidates complexity into a single coordinating system rather than requiring separate systems for each function.
3Measurement precision
If automated ML analysis is implemented, then diagnostic accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing dental images and preparing them for ML analysis before actual diagnosis. The system also performs cluster analysis on patient datasets in advance to identify patterns and correlations, reducing the time required for real-time diagnostic decision-making.
Solution Approach 2:
The patent implements continuous useful action through automated ML inference that processes images without interruption. The system maintains continuous operation by automatically managing the workflow from image input through anatomy identification, pathology detection, and treatment recommendation without manual intervention gaps.
Data Source
AI summary
Data mining of a dental image is described. In an example scenario, an aggregator service receives a dental image of a patient from a dental image provider. The dental image is processed with a machine learning (ML) anatomy dataset. An anatomy from the ML anatomy dataset is identified and matched to the dental image. The dental image is next matched and identified with a ML pathology and treatment dataset. A pathology and a treatment from the ML pathology and treatment dataset are matched to the dental image. Next, the dental image and the anatomy, the pathology, and the treatment associated with the dental image are inserted to a patient dataset associated with the patient. A cluster analysis of the patient dataset is performed with a cluster dataset to produce a correlated dental image information. The correlated dental image information is provided to a data mining entity and to compile a diagnostic aid for a user.