Distributed AI Training for Medical Imaging Offline Diagnosis
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Solution Overview
Problem
Medical imaging devices in remote locations face challenges in diagnosing medical images due to lack of network connectivity, leading to significant time lags in diagnosis, which is inconvenient for healthcare providers and patients, especially in emergency situations.
Innovation Solution
Implementing a distributed training system where local AI systems extract image or model parameters from medical images and send them to a global AI system for training, maintaining data privacy and enabling offline prediction, with periodic updates from the global AI model when connectivity is available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical imaging devices are deployed in remote locations without network connectivity, then device accessibility and deployment flexibility are improved, but diagnostic speed and time-to-result deteriorate due to significant time lags
Solution Approach 1:
The system divides the AI diagnostic functionality into two segments: a global AI system that receives and processes images remotely when connectivity is available, and a local AI model that operates autonomously at the medical imaging device when connectivity is unavailable. This segmentation allows the system to maintain deployment flexibility in remote locations while minimizing diagnostic time lags through local offline capability.
Solution Approach 2:
The system performs preliminary actions by training the local AI model in advance using the global AI system when network connectivity is available. The local model learns from the global system's diagnostic capabilities and can then operate independently offline, enabling fast local analysis without network dependency and eliminating wait times for remote diagnoses.
2Measurement precision
If medical images are transmitted to the global AI system for analysis, then diagnostic accuracy is improved, but network resource consumption and transmission time increase
Solution Approach 1:
The system extracts essential diagnostic capabilities from the global AI system and embeds them into the local AI model. Instead of transmitting entire high-resolution medical images over the network, only necessary image data or features are processed locally, reducing network resource consumption while maintaining diagnostic accuracy through the pretrained local model's ability to perform analysis offline.
3Measurement precision
If the global AI system is used for all medical image analysis, then model accuracy and sensitivity are improved, but data privacy risks and network dependency increase
Solution Approach 1:
The system implements local quality by deploying a personalized local AI model at each medical imaging device that operates independently with high accuracy. Each local model is trained on the global AI system when connectivity is available, enabling it to maintain high diagnostic precision while processing images locally without transmitting patient data over the network, thereby eliminating data privacy risks associated with cloud transmission.
4Ease of operation
If local AI models are trained independently without global system updates, then offline operation capability is improved, but model accuracy and sensitivity deteriorate over time
Solution Approach 1:
The system implements periodic action by establishing scheduled updates where the local AI model periodically synchronizes with the global AI system whenever network connectivity becomes available. This periodic synchronization allows the local model to maintain offline operation capability for continuous diagnostic service while periodically refreshing its accuracy and sensitivity through updates from the globally trained model.
Solution Approach 2:
The system implements feedback mechanisms where the local AI model's performance data and diagnostic results are periodically transmitted to the global AI system when connectivity is available. The global system uses this feedback to further train and improve the model, which then updates the local model in subsequent synchronization cycles, ensuring continuous improvement of sensitivity and accuracy while maintaining offline operational capability.
Data Source
AI summary
The present subject matter provides systems and methods for distributed training of artificial intelligence (AI) systems for medical image analysis. The systems allow for offline prediction of abnormalities of medical images on a local AI system associated with an imaging device (102). In one example, the local AI system (100) extracts image parameters from the analyzed medical images and sends it to a global AI system (120) for training a global AI model. In another example, the local AI system (100) retrains a previously trained local AI model based on the image parameters to obtain a local AI model. Model parameters are extracted from the local AI model and are sent to the global AI system to retrain a global AI model. The retrained global AI model is deployed back to the local AI system (100) for updating the local AI model.