End-to-End Deep Learning Model for Blood Vessel Condition Prediction
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Solution Overview
Problem
Current non-invasive methods for determining blood vessel condition parameters, such as fractional flow reserve (FFR), face challenges including high costs and risks associated with invasive measurements, and existing machine learning approaches require complex calculations and manual intervention, limiting their clinical applicability.
Innovation Solution
An end-to-end deep learning model combining a data flow neural network, a recursive neural network, and a conditional random field model is used to predict sequences of blood vessel condition parameters directly from image data, eliminating the need for discrete modules and enabling global optimization, faster calculation, and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CFD calculation and simulation are used to simulate blood flow, then measurement precision is improved, but calculation time increases significantly
Solution Approach 1:
The patent pre-trains a deep learning model using CFD simulation data during the development phase. Once trained, the model can quickly predict blood vessel condition parameters from medical images without performing time-consuming CFD calculations during actual clinical use. This preliminary training action enables fast inference while maintaining the accuracy of CFD-based measurements.
Solution Approach 2:
The patent creates a computational model (deep learning network) that copies and learns the complex relationships between blood vessel geometry, flow patterns, and hemodynamic parameters from CFD simulations. Instead of performing actual CFD calculations for each patient, the system uses the pre-trained model to replicate CFD results much faster, effectively copying the computational logic without the computational burden.
2Measurement precision
If conventional machine learning modules are used separately, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple separate machine learning modules (feature extraction, parameter prediction, smooth post-processing) into a single integrated end-to-end deep learning model. This unified architecture processes medical images directly to output blood vessel condition parameters without requiring separate processing stages, thereby reducing system complexity while maintaining or improving prediction accuracy through holistic optimization.
Data Source
AI summary
The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.