Cardio AI Assistant Semantic Image Analysis Workflow

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Manual evaluation of medical images in cardiovascular imaging workflows is time-consuming and inefficient, requiring clinicians to spend significant time searching, navigating, and arranging images for clinical review, which increases workload and costs.

Innovation Solution

A cardio AI smart assistant uses machine learning-based networks for semantic image analysis to generate metadata, facilitating efficient review and increased diagnostic accuracy by classifying views, detecting anatomical landmarks, assessing image quality, and providing feedback for image acquisition.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual evaluation of medical images is performed by clinicians, then diagnostic review can be conducted, but time consumption and workload increase significantly

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidtime for image review

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

An AI assistant serves as an intermediary between the medical images and clinicians, automatically generating metadata including view classification, anatomical landmark detection, image quality assessment, and completeness evaluation. This intermediary process handles time-consuming tasks such as image navigation, arrangement, and preliminary analysis, allowing clinicians to focus on diagnostic interpretation without sacrificing accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary actions by automatically analyzing images and generating comprehensive metadata before clinician review. Tasks such as image classification, landmark detection, and quality assessment are completed in advance, preparing organized and annotated image sets that reduce the time clinicians need to spend on initial image evaluation while maintaining diagnostic precision

Inventive Principle:

Principle #10Preliminary action

2Reliability

If clinicians manually search, navigate, and arrange images for review, then complete image evaluation can be achieved, but productivity decreases

Engineering Contradiction:

Improvecompleteness of image reviewVSAvoidefficiency of image review

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs self-service by automatically conducting image analysis tasks that would otherwise require manual clinician intervention. The AI assistant independently completes view classification, detects anatomical landmarks, assesses image quality, and evaluates study completeness, generating structured metadata that ensures comprehensive review coverage while significantly improving productivity through automation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical processes of image searching, navigating, and arranging are replaced with automated computational systems. The AI assistant uses machine learning algorithms to perform image analysis and metadata generation, substituting the manual mechanical workflow with an automated digital process that maintains review completeness while enhancing productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If multiple semantic image analysis tasks are performed using separate machine learning networks, then task specialization is achieved, but device complexity increases

Engineering Contradiction:

Improveanalysis accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Multiple separate machine learning networks for different semantic image analysis tasks are merged into a single unified multi-task learning network. This consolidated architecture simultaneously performs view classification, anatomical landmark detection, image quality assessment, and completeness evaluation, reducing system complexity while maintaining the specialized analytical capabilities needed for accurate medical image analysis

Inventive Principle:

Principle #5Merging (Combining)

4Productivity

If automated metadata generation is implemented, then manual workload is reduced, but implementation complexity increases

Engineering Contradiction:

Improveworkflow efficiencyVSAvoidsystem implementation complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

A single multi-functional AI assistant system performs multiple semantic image analysis tasks simultaneously through multi-task learning. This universal system handles view classification, landmark detection, quality assessment, and completeness evaluation in one integrated process, reducing manual workload and improving workflow efficiency while managing implementation complexity through a unified rather than fragmented architecture

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12175668B2Cardio AI smart assistant for semantic image analysis of medical imaging studies

Publication Date: 2024.12.24 SIEMENS HEALTHINEERS AG

AI summary

Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.