Medical Image Storage Using ML-Based Retention Detection
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Solution Overview
Problem
Existing medical image storage systems face challenges in managing high-resolution and material discrimination images, leading to potential deletion of critical images from transitory storage regions, which are not adequately addressed by current techniques.
Innovation Solution
An information processing device employs machine learning methods to detect predetermined medical information, automatically moving medical images from transitory to long-term storage when specific conditions are met, reducing the risk of image deletion and user burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If medical images are stored in the transitory storage region to manage storage capacity, then storage capacity is improved, but the risk of image deletion increases
Solution Approach 1:
The system performs preliminary actions by automatically detecting medical information and moving images to long-term storage before deletion occurs. The processor proactively identifies images requiring retention and initiates transfer to the long-term storage region, preventing the harmful effect of deletion before it can occur.
Solution Approach 2:
The system implements feedback through continuous monitoring of medical images in the transitory storage region. The processor detects medical information, evaluates retention requirements, and provides feedback by moving identified images to long-term storage. This closed-loop system ensures images are retained based on actual medical content rather than static storage policies.
2Ease of operation
If automatic storage policies are implemented to reduce user burden, then ease of operation is improved, but loss of information may occur
Solution Approach 1:
The system performs self-service by automatically detecting medical information and making retention decisions without requiring user intervention. The processor independently evaluates images, identifies those requiring long-term storage, and executes transfers autonomously, significantly reducing the operational burden on users while maintaining accurate information retention.
Solution Approach 2:
The patent replaces manual user-based storage decisions with an automated machine learning-based system. Instead of relying on users to manually identify and store important images, the system uses computational methods to detect medical information and automatically manage storage, eliminating the mechanical process of manual intervention while improving retention accuracy.
3Reliability
If machine learning-based automatic movement is implemented, then image retention accuracy is improved, but device complexity increases
Solution Approach 1:
The processor performs multiple functions within a single integrated system. It simultaneously detects medical information, evaluates retention requirements, determines movement decisions, and executes storage transfers. This multi-functional approach consolidates what could be separate complex systems into one unified processor, managing device complexity while maintaining high retention accuracy.
Data Source
AI summary
An aspect of the present invention provides an information processing device and an operation method of an information processing device, which can appropriately store a medical image. An information processing device according to an aspect of the present invention is an information processing device including a processor, in which the processor is configured to: acquire medical data including at least one of a medical image or medical comments on findings related to the medical image; apply a machine learning method to the medical data to detect predetermined medical information from the medical data; and move the medical image stored in a transitory storage region of a storage device to a long-term storage region of the storage device in a case where the medical information is detected.