AI Surgical Intervention Image Prediction for Emergency Decisions
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Solution Overview
Problem
Delays in interpreting imaging studies during surgical emergencies can lead to poor outcomes, and existing AI technologies may provide inaccurate assessments, resulting in false positives or negatives regarding the necessity for emergent surgical intervention.
Innovation Solution
A system utilizing artificial intelligence to predict the need for emergent surgical intervention by training models on patient attributes from historical events, including imaging studies, and generating output images based on these predictions, deployable in healthcare systems to enhance timely and accurate decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists manually interpret imaging studies during surgical emergencies, then diagnostic accuracy can be maintained, but time delays occur that lead to poor patient outcomes
Solution Approach 1:
The patent introduces an AI-based image analysis system as an intermediary between the imaging study and the radiologist. This intermediary automatically pre-analyzes the images, generates preliminary findings, and prioritizes cases, thereby reducing the time burden on radiologists while maintaining diagnostic accuracy through human oversight of AI-generated results.
Solution Approach 2:
The system performs preliminary image interpretation and triage before radiologist review. By automatically detecting critical findings, generating initial reports, and prioritizing urgent cases, the system prepares the workflow in advance, allowing radiologists to focus on confirmation and complex cases, thus reducing overall interpretation time without sacrificing accuracy.
2Productivity
If AI technologies are used to assess imaging studies, then interpretation speed can be improved, but accuracy may deteriorate resulting in false positives or negatives
Solution Approach 1:
The patent implements a feedback loop where AI-generated findings are reviewed and verified by radiologists, and the results are fed back to continuously improve the AI system. This human-in-the-loop approach allows the system to learn from corrections and adjustments, progressively reducing false positives and negatives while maintaining high interpretation speed.
Solution Approach 2:
The system merges AI automated analysis capabilities with radiologist expert review in a hybrid workflow. The AI handles rapid initial assessment and pattern recognition, while radiologists provide contextual understanding and confirmation, combining the speed of machines with the accuracy of human expertise to achieve both high productivity and precision.
3Measurement precision
If multiple imaging studies and patient attributes are analyzed comprehensively, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modules: image preprocessing, feature extraction, multi-modal data integration, model inference, and result generation. Each module handles a specific aspect of the analysis independently, making the overall system more manageable and maintainable while still achieving comprehensive analysis for high prediction accuracy.
Solution Approach 2:
The system employs a universal deep learning framework that can process multiple types of imaging studies (CT, MRI, X-ray) and various patient attributes through a single integrated model architecture. This multi-functional approach allows comprehensive data analysis without requiring separate complex systems for each data type, thereby maintaining accuracy while controlling overall system complexity.
Data Source
AI summary
A system that includes data collection engine devices, client devices and backend devices. The backend devices include trained models, business logic, and attributes of a plurality of patient events. A plurality of data collection engines and hospital information systems send input attributes of new patient events to the backend devices. The backend devices can generate output images predicting particular outcomes of new patient events based upon the input attributes utilizing the trained models.