Simulated 3D Radiological Datasets for AI Training
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Solution Overview
Problem
Current AI algorithms in diagnostic radiology face challenges in detecting small diagnostic features within large datasets, such as in MRI images, which are crucial for early cancer detection to reduce treatment costs and improve survival rates.
Innovation Solution
The method involves generating simulated 3D radiological datasets using generative adversarial networks to train and test AI algorithms, allowing for the creation of realistic volumetric datasets that mimic real medical imaging examinations, including various anatomical and pathological conditions, to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI algorithms analyze large medical image datasets to detect small diagnostic features, then detection capability improves, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary actions by generating simulated 3D radiological datasets using generative adversarial networks before actual analysis. These synthetic datasets are created in advance to train AI algorithms, allowing the system to pre-learning from diverse anatomical and pathological variations without requiring extensive real patient data collection and processing time
Solution Approach 2:
The patent creates synthetic copies of real medical images through generative adversarial networks. These copied datasets mimic real medical imaging examinations including various anatomical and pathological conditions, allowing AI training without using actual patient data, thus reducing time consumption while maintaining detection accuracy
2Reliability
If more real medical data is collected for training AI algorithms, then model performance improves, but data privacy concerns and collection costs increase
Solution Approach 1:
The system creates synthetic copies of real medical datasets using generative adversarial networks. These copied datasets preserve the statistical properties and diagnostic features of real medical images while being completely synthetic, eliminating the need for complex data collection processes and addressing privacy concerns while maintaining model performance
Solution Approach 2:
The generative adversarial network acts as an intermediary between the need for training data and patient privacy requirements. It generates intermediate synthetic data that captures essential diagnostic features without containing real patient information, thus mediating between model performance needs and privacy protection
3Adaptability or versatility
If AI algorithms are trained on diverse pathological conditions, then diagnostic versatility improves, but training data requirements increase
Solution Approach 1:
The system performs preliminary generation of diverse pathological cases through generative adversarial networks before actual training. The GAN can synthesize various anatomical and pathological conditions on demand, providing unlimited diverse training data without requiring proportional increases in real patient data collection
Solution Approach 2:
The patent creates synthetic copies representing diverse pathological conditions by training the GAN on existing diverse datasets. Once trained, the GAN can generate unlimited variations of different pathologies, anatomical structures, and disease presentations, providing extensive training data volume without collecting proportional real patient data
Data Source
AI summary
This patent includes a method and apparatus for the generation of a simulated, realistic 3D radiological dataset from CT, MRI, PET, SPECT or DTS examinations. This simulated dataset can be segmented, filtered, manipulated, used with artificial intelligence algorithms and viewed in conjunction with head display units and geo-registered tools.