AI Annuloplasty Ring Selection via Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current annuloplasty ring selection methods for heart valves are challenging due to the complex structure of heart valves, making it difficult for physicians to accurately choose the appropriate size, shape, material, and suture features, often leading to suboptimal results such as regurgitation or the need for additional surgeries.
Innovation Solution
A system that captures image data of the heart valve, processes it to identify anatomical features, and generates data for determining the most suitable annuloplasty ring characteristics, including size, shape, and flexibility, using machine-learning techniques to recommend the optimal ring for implantation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used for annuloplasty ring selection, then the process is simple and quick, but the selection accuracy is low leading to suboptimal results
Solution Approach 1:
The patent replaces manual visual inspection and physical measurement with an automated imaging system that captures images of the heart valve and uses image processing algorithms to automatically identify anatomical features and calculate measurements. This substitution of mechanical/manual processes with automated optical and computational systems directly improves measurement precision while managing the introduced complexity through software automation.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a bridge between the raw heart valve anatomy and the final annuloplasty ring selection. The system uses fiduciary markers as intermediary reference points to establish scale and geometry, and employs computational algorithms as intermediaries to translate visual data into precise measurements, thereby improving selection accuracy without requiring direct manual measurement.
2Measurement precision
If multiple annuloplasty ring characteristics are considered, then the selection accuracy improves, but the decision-making process becomes more complex
Solution Approach 1:
The patent segments the complex selection process into distinct analytical components: image capture, fiduciary marker identification, anatomical feature detection, measurement calculation, and ring specification generation. By dividing the overall decision-making process into these manageable segments, the system can evaluate multiple characteristics (annulus area, circumference, shape metrics) systematically while presenting the final synthesized recommendation in an easily interpretable format.
Solution Approach 2:
The system incorporates feedback mechanisms by comparing the measured heart valve characteristics against known optimal parameters for different annuloplasty ring types. The image processing system provides feedback on the accuracy of feature identification and measurement, allowing for iterative refinement of the selection process while guiding the physician toward the most appropriate ring choice based on quantitative criteria.
3Measurement precision
If machine-learning techniques are used, then the selection accuracy improves through trained models, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of heart valve images and annuloplasty ring outcomes before clinical use. The fiduciary marker system is pre-configured with known geometric relationships, and the image processing algorithms are pre-calibrated with reference measurements. This preliminary preparation allows the system to perform rapid, accurate selections during actual clinical procedures without requiring extensive real-time computation or model training.
Data Source
AI summary
Techniques relate to analyzing image data to determine an annuloplasty ring to implant for an annuloplasty procedure. For example, image data can be received that depicts a heart valve. The image data can be analyzed to identify one or more image features that represent one or more anatomical features of the heart valve. Based on the one or more image features, heart data can be generated that indicates a measurement and/or another characteristic of the heart valve. An annuloplasty ring can be determined based on the heart valve data and/or annuloplasty ring data indicating characteristics of one or more annuloplasty rings. User interface data can then be generated that indicates the annuloplasty ring.