Medical Information Processing Apparatus for Gene Mutation Classification Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Gene mutation classification models often fail to provide accurate and consistent results, leading to potentially oncologically invalid classifications, which can result in incorrect monitoring of gene mutations and therapeutic decisions.
Innovation Solution
A medical information processing apparatus and method that acquires and compares gene mutation classification results from multiple time phases to evaluate the consistency of the classification model based on medical validity, calculating a consistency score to assess the model's reliability and selecting the most consistent model for clinical application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a gene mutation classification model is used to classify gene mutations from radiation images, then non-invasive monitoring of gene mutations is enabled, but the model may conduct oncologically invalid classification and return incorrect results
Solution Approach 1:
The patent implements a feedback mechanism by comparing classification results across multiple time phases and using gene validity knowledge to evaluate model consistency. The system feeds back consistency evaluation results to assess model reliability, allowing clinicians to identify and correct potentially invalid classifications while maintaining the non-invasive monitoring advantage.
2Productivity
If a gene mutation classification model is applied for monitoring, then therapeutic decision support is provided, but the model may return classifications representing very different tendency or extinction of gene mutation despite absence of therapeutic action
Solution Approach 1:
The patent applies preliminary action by establishing a gene validity knowledge base before model application. This knowledge base contains predetermined valid change patterns (acquisition, continuation, extinction) that are used to evaluate model outputs in advance. By having these validation rules prepared beforehand, the system can quickly assess whether classification results are oncologically valid without delaying therapeutic decision support.
3Reliability
If multiple time phase images are input into the classification model to evaluate consistency, then model reliability assessment is enabled, but additional image acquisition and processing requirements increase
Solution Approach 1:
The patent makes the classification model multi-functional by enabling it to serve both its primary purpose (gene mutation classification) and a secondary purpose (consistency evaluation). The same model processes images from multiple time phases for both diagnostic classification and reliability assessment, eliminating the need for separate evaluation systems and reducing overall device complexity.
4Reliability
If consistency evaluation based on medical validity is implemented, then oncologically valid classifications are identified, but additional computational processing is required to calculate consistency scores
Solution Approach 1:
The patent segments the consistency evaluation process into distinct computational stages: (1) comparing classification results across time phases, (2) determining change types (acquisition, continuation, extinction), (3) evaluating against validity knowledge base, and (4) calculating consistency scores. This segmentation allows the system to process only relevant data at each stage, reducing overall computational load while ensuring thorough medical validity assessment.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
According to one embodiment, a medical information processing apparatus includes processing circuitry. The processing circuitry acquires first and second medical images acquired in at least first and second time phases. The processing circuitry inputs the first and second medical images into a gene mutation classification model to generate first and second gene mutation classification results. The processing circuitry judges consistency of the gene mutation classification model based on the first and second gene mutation classification results.