Deep Learning Network Emulation for Medical Imaging Optimization
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Solution Overview
Problem
Healthcare facilities face challenges in providing quality care due to economic, technological, and administrative hurdles, including limited staff skills, equipment complexity, and the need for standardized radiation exposure dose management, which complicates the effective use of imaging and information systems for patient examination, diagnosis, and treatment.
Innovation Solution
A deep learning network system is designed to emulate and improve medical imaging systems by training models using known inputs and outputs, simulating system behavior, and generating recommendations for configuration and structure optimization, facilitating improved image acquisition, reconstruction, and diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning network models are trained using known input and output data to simulate system behavior, then diagnostic accuracy is enhanced, but system complexity increases
Solution Approach 1:
The patent creates digital twins (virtual copies) of medical imaging systems by training deep learning network models on known input-output data. These digital twins simulate system behavior, allowing virtual experimentation and analysis without manipulating physical equipment, thereby improving diagnostic accuracy while avoiding the complexity of physical system modifications
Solution Approach 2:
The patent replaces physical medical imaging systems with their digital counterparts (digital twins) for simulation and analysis purposes. Instead of physically modifying imaging equipment to improve diagnostic accuracy, the system creates virtual models that can be extensively simulated and optimized without physical constraints
2Productivity
If digital twins are created to simulate system behavior and generate recommendations, then workflow efficiency is improved, but computational resources and time are consumed
Solution Approach 1:
The patent pre-trains deep learning network models using historical medical imaging data and known outcomes before actual diagnostic tasks. This preliminary training allows the digital twins to make rapid predictions and generate recommendations during real-time workflows without consuming significant computational resources at the time of use
Solution Approach 2:
The digital twin system automatically simulates system behavior and generates its own recommendations for optimization without requiring continuous human intervention. The trained models self-evaluate and produce diagnostic suggestions, improving workflow efficiency while minimizing the time investment required for ongoing analysis
Data Source
AI summary
Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.