AI Echocardiogram Loop Prioritization for Diagnostic Accuracy
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Solution Overview
Problem
Echocardiogram analysis is inefficient due to the need for cardiologists to manually review numerous ultrasound loops of varying quality, making it time-consuming and prone to errors, as technicians' subjective positioning and image quality affect diagnosis accuracy.
Innovation Solution
An AI engine is used to generate confidence scores for diagnostic codes and identify the most relevant echocardiogram loops, reducing the number of loops that need to be reviewed by cardiologists and providing a user interface to confirm or reject preliminary diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cardiologists manually review numerous echocardiogram loops to ensure accurate diagnosis, then diagnostic accuracy is improved, but time consumption and workload increase significantly
Solution Approach 1:
The patent segments the large set of echocardiogram loops into smaller relevant subsets using AI-based view classification. Instead of reviewing all loops, the system identifies and presents only those loops that are relevant to each diagnostic code, thereby reducing the review burden while maintaining diagnostic accuracy.
Solution Approach 2:
The system performs preliminary AI-based analysis to generate diagnostic codes and classify loops by view type before the cardiologist reviews them. This preliminary action prepares the data in advance, allowing the cardiologist to focus only on confirming or rejecting pre-generated codes rather than creating diagnoses from scratch.
2Reliability
If technicians acquire multiple loops for each view to ensure adequate coverage, then diagnostic completeness is improved, but the number of loops to be reviewed increases
Solution Approach 1:
The patent extracts only the relevant loops from the complete set of acquired loops based on AI-based view classification. By taking out only those loops that are relevant to each diagnostic code, the system reduces the review workload while ensuring diagnostic completeness for each specific code.
Solution Approach 2:
The system applies different levels of review intensity to different loops based on their relevance to specific diagnostic codes. Loops that are highly relevant to a diagnostic code receive focused attention, while less relevant loops are either excluded or given lower priority, optimizing the review process.
3Loss of information
If all acquired loops are presented for review, then no relevant information is missed, but the complexity and time required for interpretation increases
Solution Approach 1:
The patent segments the complete set of loops into multiple subsets organized by diagnostic code and view type. This segmentation reduces interpretation complexity by allowing cardiologists to review loops in organized groups rather than as a single large set, while maintaining information completeness through systematic coverage.
Solution Approach 2:
The system performs partial review by focusing on subsets of loops that are most relevant to each diagnostic code rather than reviewing all loops equally. This partial action approach ensures that sufficient information is reviewed to make accurate diagnoses without the excessive complexity of reviewing every single loop.
Data Source
AI summary
In an echocardiogram analysis method, a diagnostic code is received or generated for an echocardiogram comprising a set of echocardiogram loops. A plurality of different subsets are selected from the echocardiogram. Each subset consists of one or more echocardiogram loops of the set of echocardiogram loops. For each subset, a confidence score indicating relevance of the subset to the diagnostic code is determined using an artificial intelligence (AI) engine operating on the subset, where the AI engine is trained on historical echocardiograms labeled with diagnostic codes. A relevant group of echocardiogram loops is identified based on the determined confidence scores for the respective subsets indicating relevance of the respective subsets to the diagnostic code. An echocardiogram reading user interface is presented, including displaying the diagnostic code associated with the echocardiogram loops of the relevant group of echocardiogram loops.