Cardiac Catheterization Image Analysis for Automated SYNTAX Scoring
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Solution Overview
Problem
Conventional cardiac catheterization image recognition methods require significant manual interpretation by doctors, consuming time and resources, and yield inconsistent results due to the reliance on human expertise, affecting the efficiency and accuracy of cardiac anatomy scoring.
Innovation Solution
A cardiac catheterization image recognition and evaluation method utilizing deep learning algorithms, including U-Net and YOLO v4 for object recognition, image processing, and Mask RCNN and Bi-LSTM for occlusion detection, to automate the identification of vessel types and occlusion rates, enabling accurate cardiac anatomy scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of cardiac catheterization images by doctors is used, then accurate judgment of vessel occlusion can be achieved, but it consumes significant time and human resources
Solution Approach 1:
The patent replaces the manual mechanical interpretation process with an automated deep learning system. The neural network model automatically analyzes cardiac catheterization images to identify vessel occlusions, calculate occlusion rates, and generate SYNTAX scores, substituting the doctor's manual analysis with an automated computational system that processes images rapidly while maintaining diagnostic accuracy
Solution Approach 2:
The patent creates a digital copy of the diagnostic process through trained neural network models that replicate expert doctor analysis. The model is trained on numerous labeled images to learn and copy the pattern recognition and judgment capabilities of experienced cardiologists, enabling automated interpretation that mirrors manual expert analysis without requiring actual doctor involvement in each case
2Productivity
If multiple deep learning algorithms and image processing steps are implemented, then automation efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex image analysis task into distinct functional modules: a first deep learning algorithm for object recognition and vessel identification, image processing steps for enhancement and feature extraction, a second deep learning algorithm for occlusion detection, and calculation modules for determining occlusion rates and SYNTAX scores. This segmentation allows each component to be optimized independently while working together to achieve high automation efficiency
Solution Approach 2:
The patent creates a multi-functional integrated system where the processing apparatus performs multiple functions: automatic vessel identification, occlusion detection, occlusion rate calculation, and SYNTAX score generation. By combining these functions into a single automated workflow, the system achieves high productivity without requiring separate manual processes for each function, effectively managing complexity through functional integration
Data Source
AI summary
A cardiac catheterization image recognition and evaluation method is disclosed. The first deep learning algorithm is used to conduct an object recognition process on the cardiac catheterization image to obtain the vessel object image. The image processing process is conducted to the cardiac catheterization image to obtain the vessel location image. The vessel object image and the vessel location image are combined to obtain the vessel contour image. The vessel type judging process is conducted to the vessel contour image to determine the type of vessel in the cardiac catheterization image. The second deep learning algorithm is used on the vessel contour image to detect the vessel occlusion location and to judge the vessel occlusion rate. Based on the type of vessel and the vessel occlusion rate at the vessel occlusion location, the cardiac catheterization image is evaluated to obtain the SYNTAX Score.