Neural Network Dental Imaging for Lesion Risk Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Human error and inefficiency in dental diagnosis and remedial procedures due to the difficulty in visually observing small dental dimensions and lesions, leading to misdiagnosis and varying treatment plans, especially in cases involving interproximal invasions and lesions.
Innovation Solution
A device using dental imagery and neural networks to measure critical dimensions and calculate risk classifications, employing techniques like convolutional neural networks (CNN) for lesion and interproximal invasion detection, and integrating factors like gum health and medical conditions to automate diagnosis and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual observation and manual measurement are used for dental diagnosis, then the process is simple and requires no additional equipment, but the measurement precision is insufficient for small dimensions and lesions leading to misdiagnosis
Solution Approach 1:
The patent replaces manual visual observation and mechanical measurement with an automated image processing system using convolutional neural networks (CNN) and image segmentation algorithms. The system processes digital images of teeth to automatically detect, segment, and measure lesions and interproximal invasions, eliminating the limitations of human visual perception for small dimensions while avoiding complex manual measurement procedures.
Solution Approach 2:
The patent introduces digital imagery and computational algorithms as intermediaries between the dentist and the tooth structure. Instead of direct visual observation, the system uses captured images as intermediaries that can be processed by neural networks to extract precise dimensional information about lesions and defects, enabling accurate measurement without direct manual intervention.
2Reliability
If multiple dentists provide opinions for comprehensive diagnosis, then the reliability of diagnosis improves, but the time required for diagnosis increases and productivity decreases
Solution Approach 1:
The patent implements self-service through automated diagnosis where the image processing system independently performs comprehensive analysis without requiring multiple dentists. The convolutional neural network automatically detects, segments, and measures all relevant features including lesions, interproximal invasions, and gum health, providing consistent and reliable diagnosis in a single processing operation rather than requiring multiple professional opinions.
Solution Approach 2:
The system provides continuous and consistent diagnostic analysis by repeatedly processing images through the neural network to ensure comprehensive evaluation. The automated process maintains continuous attention to all diagnostic criteria without the interruptions and variations inherent in multiple human reviewers, thereby maintaining high reliability while improving efficiency.
3Productivity
If junior dentists work independently to increase productivity, then the speed of diagnosis increases, but the reliability of diagnosis decreases due to lack of experience
Solution Approach 1:
The patent introduces an automated image processing system as an intermediary that bridges the gap between junior dentists' productivity and the reliability expected from experienced practitioners. The convolutional neural network serves as a mediator that provides consistent, experience-based diagnostic guidance to junior dentists, enabling them to work independently at high speed while maintaining diagnostic reliability through the system's algorithms trained on extensive dental data.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network continuously refines its analysis based on the input images and compares findings against established diagnostic criteria. This feedback loop ensures that even when junior dentists use the system, the diagnostic reliability is maintained through the algorithm's consistent application of expert knowledge, while the system operates at the speed necessary for high productivity.
Data Source
AI summary
Systems for detecting and measuring certain dental defects and/or existing restorations, such as lesions which may or may not invade interproximal areas, using a neural network is disclosed. In some embodiments, a patient's health conditions and certain dental conditions such as crowding, presence of implants, gum disease, etc. are also considered by an automated system to classify the dental health of a patient into one or more risk classes. The system then suggests appropriate remedial measures and an appropriate maintenance program for that patient to keep therapy within guidelines of proven standard of care, leading the way to reduce tooth loss in population.