Adversarial Dental Image Analysis for Robust Diagnosis

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Solution Overview

Problem

Current dental image analysis technologies face challenges in automating the diagnosis and treatment planning process due to issues with image quality, orientation, and the integration of diverse imaging modalities, leading to inefficiencies and potential misdiagnoses.

Innovation Solution

A system utilizing machine learning models, specifically convolutional neural networks (CNNs), for pre-processing dental images to correct orientation, remove contamination, and classify features, combined with adversarial training to enhance robustness against image manipulation, enables accurate diagnosis and treatment planning across different imaging modalities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated image analysis is implemented, then diagnostic efficiency is improved, but reliability deteriorates due to potential misdiagnoses and vulnerability to adversarial attacks

Engineering Contradiction:

Improvediagnostic efficiencyVSAvoiddiagnostic accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies adversarial training by exposing the neural network to deliberately manipulated adversarial images during the training phase. This preliminary anti-action allows the model to learn defensive strategies against various attack types (pixelation, noise, occlusion, geometric transformations) before deployment, thereby maintaining diagnostic reliability while preserving automation efficiency

Inventive Principle:

Principle #9Preliminary anti-action

Solution Approach 2:

The system implements multiple defensive mechanisms as protective layers around the core diagnostic model. These include adversarially trained ensemble models, input preprocessing filters, and post-processing validation steps that cushion against potential adversarial attacks, ensuring that even if one layer is compromised, the overall diagnostic reliability remains intact

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

2Adaptability or versatility

If multiple imaging modalities are integrated, then comprehensive analysis is improved, but device complexity worsens

Engineering Contradiction:

Improveimaging modality integrationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system employs a universal adversarial training framework that can handle multiple imaging modalities (intraoral images, bitewing images, panoramic images, CBCT images) through a single unified neural network architecture. The model is trained to recognize dental pathologies across different image types and formats, eliminating the need for separate specialized models for each modality and thereby reducing overall system complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces standardized preprocessing and normalization layers as intermediary components between diverse imaging modalities and the core diagnostic network. These intermediary layers convert various image formats and resolutions into a unified representation that the neural network can process efficiently, simplifying the integration of multiple imaging sources

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If image pre-processing is applied, then measurement precision is improved, but loss of information worsens due to aggressive contamination removal

Engineering Contradiction:

Improvefeature detection accuracyVSAvoidimage detail loss

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system dynamically adjusts preprocessing parameters based on the specific characteristics of each input image and the type of contamination detected. Rather than applying fixed aggressive filtering, the model modulates the strength and type of preprocessing operations to minimize information loss while still achieving sufficient noise reduction for accurate pathology detection

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11311247B2System and methods for restorative dentistry treatment planning using adversarial learning

Publication Date: 2022.04.26 RETRACE LABS

AI summary

Training a generator includes processing a dental image using the generator to obtain a synthetic pathology label, such has a pixel mask indicating portions of the dental image representing caries. The synthetic pathology label is compared to a target pathology label for the dental image and the generator is updated according to the comparison. The synthetic pathology may be evaluated by a discriminator along with a real pathology label to obtain a realism estimate. The discriminator and generator may be updated according to accuracy of the realism estimate. Inputs to the generator may further include tooth labels and/or labels of restorations. Machine learning models may be trained to label restorations and defects in restorations. A machine learning model may be trained to identify the surface of a tooth having a pathology thereon.