AI Prediction of Emergent Surgical Need From Imaging Data
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Solution Overview
Problem
Delays in interpreting imaging studies and communicating surgical needs during emergencies can lead to poor outcomes in neurological surgery, with radiologists and physicians often providing inaccurate assessments.
Innovation Solution
A system utilizing artificial intelligence to predict the need for emergent surgical intervention by training models on patient attributes from historical events, generating timely and accurate predictions based on imaging studies and text input, and providing notifications to surgical teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists and physicians manually interpret imaging studies and communicate surgical needs, then human expertise and judgment are applied, but delays occur and accuracy decreases
Solution Approach 1:
An AI system acts as an intermediary between imaging data and surgical decision-making. The system receives imaging data, automatically analyzes it using trained machine learning models, generates predictions about surgical needs, and transmits these predictions to surgical teams. This intermediary processing eliminates manual interpretation delays while maintaining or improving accuracy through automated pattern recognition.
Solution Approach 2:
The manual mechanical process of radiologist/physician interpretation and communication is replaced with an automated electronic system. The AI system processes imaging data through algorithms and neural networks, substituting human cognitive mechanics with computational mechanics. This substitution dramatically reduces time while preserving diagnostic accuracy through consistent application of trained models.
2Productivity
If manual interpretation and communication processes are used, then human judgment is applied, but throughput and efficiency of the healthcare system decrease
Solution Approach 1:
The AI system performs self-service by automatically analyzing imaging data and generating surgical need predictions without requiring human intervention in the interpretation process. The system trains on historical data, independently processes new imaging studies, and outputs predictions that are immediately available to surgical teams. This self-service capability dramatically increases throughput while the automated nature simplifies the overall system complexity by eliminating manual coordination steps.
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 predict particular outcomes of new patient events based upon the input attributes utilizing the trained models.