Medical Image Data Evaluation With Patient-Specific Relevance
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Solution Overview
Problem
Current medical image data evaluation methods are time-consuming and require extensive specialist training, and existing computer-aided diagnostics often rely on rigid threshold values that can lead to inaccurate or inefficient clinical relevance assessments.
Innovation Solution
A computer-implemented method and apparatus that determine patient-specific relevance criteria for medical image data evaluation, using artificial neural networks to assess clinical relevance without fixed thresholds, incorporating patient-specific and context data to tailor evaluations to individual patient characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complete automation of medical image data evaluation is implemented, then productivity is improved, but measurement precision deteriorates due to lack of specialist expertise
Solution Approach 1:
The patent introduces a hybrid system where an artificial neural network acts as an intermediary between automated image analysis and specialist interpretation. The neural network pre-processes and classifies image data, presenting only relevant findings to specialists for final assessment, thus combining automated efficiency with human expertise for accurate diagnosis.
2Device complexity
If rigid threshold values are used for classification, then device complexity is reduced, but measurement precision deteriorates due to inability to account for patient-specific variations
Solution Approach 1:
The patent implements dynamic threshold adjustment where the classification threshold is not fixed but adapts based on patient-specific data and context. The system automatically modifies threshold values according to individual patient characteristics, making the evaluation system both simple to operate and highly accurate for each specific case.
Solution Approach 2:
The system changes the parameter of classification threshold dynamically based on patient context. Instead of using a static threshold value, the neural network adjusts the threshold parameter according to patient-specific factors, thereby maintaining measurement precision without increasing operational complexity.
3Measurement precision
If patient-specific relevance criteria are determined using artificial neural networks, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The artificial neural network is trained in advance to autonomously determine patient-specific relevance criteria without requiring complex manual configuration. The system serves itself by automatically adapting to individual patient characteristics and making clinically relevant assessments, thereby improving precision while keeping the operational complexity manageable.
Data Source
AI summary
A method for evaluation of medical image data comprises: providing medical image data of a patient to be examined; determining, for at least one segment of the medical image data, a respective classification probability value with respect to at least one classification from a list of specified classifications; determining a patient-specific relevance criterion for at least one classification for at least the at least one segment of the medical image data; and determining a clinical relevance of the at least one classification for the at least one segment of the medical image data using the patient-specific relevance criterion, and at least one of based on the classification probability values or based on the at least one segment of the medical image data.