Medical Image Processing Trained Model Workflow Automation
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Solution Overview
Problem
The existing medical information processing systems face challenges in efficiently automating the workflow from medical image acquisition to interpretation, particularly in retrieving relevant images and performing image processing tasks, which can lead to inefficiencies and delays due to the complexity of creating and managing rules for various clinical cases and patient data.
Innovation Solution
A medical information processing apparatus that utilizes a trained model to specify relevant images and image processing processes based on input medical image data and examination information, simplifying the workflow by automating the acquisition and processing tasks through supervised and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a trained model is used to specify relevant images and image processing processes, then the workflow efficiency is improved and automation is enhanced, but the initial system complexity and training requirements increase
Solution Approach 1:
The system performs preliminary actions by training the model in advance using historical workflow data before actual deployment. The model is trained offline with patient data, images, and workflow information to learn optimal image selection and processing processes, so that during actual use, the model can automatically specify relevant images and processing steps without requiring complex real-time decision logic
Solution Approach 2:
The trained model acts as an intermediary between the complex workflow requirements and the automated execution system. Instead of directly implementing complex rule-based logic, the patent introduces a machine learning model that learns from historical data and mediates the decision-making process, simplifying the system architecture while maintaining high workflow efficiency
2Loss of time
If manual retrieval and processing of relevant images is performed, then the system remains simple and controllable, but time loss and workflow delays occur
Solution Approach 1:
The system enables self-service automation where the trained model autonomously specifies relevant images and determines appropriate image processing processes without requiring manual intervention. The model automatically retrieves necessary images from the database and selects processing parameters based on learned patterns from historical workflows, significantly reducing workflow delay while achieving high automation
Solution Approach 2:
The system implements feedback mechanisms by using actual workflow outcomes to continuously improve the model. The model is trained on historical workflow data including which images were actually used and what processing was performed, allowing it to learn from past decisions and improve its automation accuracy over time, reducing errors and rework
3Adaptability or versatility
If comprehensive rules are created for all clinical cases, then the system can handle diverse scenarios, but the rule creation and management complexity increases significantly
Solution Approach 1:
Instead of creating explicit rules for each clinical scenario, the system changes the approach by training the model on diverse historical data representing various clinical cases. The model learns to adapt to different clinical scenarios by recognizing patterns in the training data, automatically adjusting its behavior based on the specific patient data, images, and clinical context without requiring manual rule creation for each case
Solution Approach 2:
The trained model serves as a universal solution that can handle multiple clinical cases and scenarios through a single system. Rather than implementing separate rule sets for different clinical situations, the model learns from diverse historical workflows and can generalize to handle various clinical cases, images types, and processing requirements with one unified system
Data Source
AI summary
A medical information processing apparatus according to an embodiment includes: a memory storing therein a trained model provided with a function to specify, on the basis of input information including a medical image and medical examination information related to the medical image, at least one selected from between a relevant image relevant to the medical image and an image processing process performed on the basis of the medical image; and processing circuitry configured to give an evaluation to at least one selected from between the relevant image and the image processing process specified by the trained model.