Echocardiogram Video Analysis for Automated Disease Detection
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Solution Overview
Problem
Current electronic health record systems are prone to data entry errors, particularly in capturing disease-related measurements from echocardiogram studies, which can lead to missed diagnoses of conditions like aortic stenosis, potentially resulting in untreated diseases and even sudden death.
Innovation Solution
The system combines medical image analysis with textual data analysis using a multimodal learning framework to extract disease-specific features from echocardiogram reports, Doppler patterns, and other data sources, employing optical character recognition and convolutional neural networks to automatically identify and extract relevant measurements and disease indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry is used in electronic health record systems, then ease of operation is maintained, but measurement precision and reliability deteriorate due to data entry errors
Solution Approach 1:
The patent replaces manual data entry mechanisms with automated optical character recognition (OCR) and image processing systems. The system automatically extracts measurement data from echocardiogram images and converts it to digital format, eliminating human error in data transcription while maintaining operational simplicity through automation.
Solution Approach 2:
The system enables self-service automation where the echocardiogram analysis system automatically extracts, validates, and enters measurement data into electronic health records without requiring manual intervention. The automated template matching and data extraction processes perform data entry functions independently, improving precision while reducing operational complexity.
2Reliability
If automated data extraction is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the data extraction process into distinct functional modules: image processing, template matching, optical character recognition, and data validation. Each module handles a specific aspect of measurement extraction, making the complex system manageable and maintainable while improving reliability through specialized processing at each stage.
Solution Approach 2:
The system changes parameters dynamically by adapting to different echocardiogram image formats, measurement types, and clinical protocols. The template matching mechanism adjusts to various layout configurations, and the OCR system adapts to different font styles and measurement notations, maintaining high reliability across diverse data formats without requiring complex manual configuration.
3Productivity
If manual review of echocardiogram findings is performed, then ease of operation is maintained, but productivity decreases due to time-consuming processes
Solution Approach 1:
The system performs preliminary automated extraction and validation of measurement data before it reaches human reviewers. The automated template matching and OCR processes pre-process the data, identifying and correcting obvious errors, which reduces the time human reviewers need to spend on manual data entry and verification, thereby increasing overall productivity.
Solution Approach 2:
The automated data extraction system operates continuously without interruption, processing echocardiogram images and extracting measurements in real-time as they are acquired. This continuous automated processing eliminates the periodic bottlenecks of manual review cycles, maintaining steady high-speed data extraction and improving productivity while the system complexity is managed through efficient algorithm design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces false positives and negatives, enabling accurate detection of aortic stenosis and other diseases, with a 96% precision rate, leading to improved patient cohort identification and timely intervention.
Implementation Method 1
optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements
Data Source
AI summary
Automatic detection of disease from analysis of echocardiographer findings in echocardiogram videos is provided. In various embodiments, a plurality of medical images containing embedded text are read. The plurality of medical images are clustered into a plurality of clusters. Medical images of a first cluster of the plurality of clusters are ranked based on the frequency of measurement names within the medical images of the first cluster. A candidate tabular template is generated indicative of a layout of measurement name/value pairs within the medical images of the first cluster. According to the candidate tabular template, optical character recognition is applied to one of the plurality of medical images of the first cluster to extract candidate measurements. The candidate tabular template and the candidate measurements are presented to a user.