Software Echocardiogram Quality Control via ML Difference Models
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Solution Overview
Problem
Current AI-based medical image analysis systems for echocardiography require manual inspection for quality control, contradicting the goal of automatic image processing and increasing the workload for medical professionals.
Innovation Solution
An automatic method using machine learning models to evaluate the accuracy of software-generated results by training a difference model to predict differences between software-tracked and adjusted contours, and an evaluation model to assess the quality of software-generated analysis results, allowing for direct use or manual adjustment based on the evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is performed for quality control, then the accuracy of software-generated results is improved, but the workload and time consumption increase
Solution Approach 1:
The patent applies preliminary action by training an evaluation model in advance using historical data containing both software-generated results and expert-adjusted results. This pre-trained model can then automatically evaluate new software-generated results without requiring real-time manual inspection, thus improving accuracy while reducing time consumption.
Solution Approach 2:
The patent introduces an evaluation model as an intermediary between the software-generated results and the final decision-making process. This model acts as a mediator that automatically assesses result quality, reducing the need for direct human intervention while maintaining high accuracy standards.
2Manufacturing precision
If manual adjustment is required for software-generated contours, then the manufacturing precision is improved, but the device complexity and operation difficulty increase
Solution Approach 1:
The patent implements feedback by using the evaluation model to provide automatic quality assessment feedback on software-generated results. This feedback mechanism identifies which results require adjustment and guides the adjustment process, improving precision while simplifying operation through automated guidance rather than requiring expert judgment for every case.
3Productivity
If automatic evaluation method is implemented, then the productivity is improved, but the reliability may be compromised without sufficient training data
Solution Approach 1:
The patent applies preliminary action by extensively training the evaluation model using historical data before deployment. This pre-training ensures the model learns from sufficient examples, improving its reliability before it is used to enhance productivity in clinical workflows.
Solution Approach 2:
The patent implements self-service by enabling the evaluation model to automatically assess its own performance and provide quality evaluations without external intervention. This automation maintains reliability through consistent application of learned criteria while significantly improving diagnostic workflow efficiency.
Data Source
AI summary
The present invention relates to a method for evaluating software-analyzed videos, comprising receiving input images and corresponding software-analyzed images, generating predicted difference parameters by at least one difference model, generating geometric parameters, and generating a predicted evaluation result based on the predicted difference parameters and the geometric parameters by an evaluation model. The present invention also relates to a method for training models to perform difference parameter generation, and a method for training models to perform evaluation result generation described above.