Dynamic Medical Image Analysis for Patient-Friendly Diagnosis Explanations
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Solution Overview
Problem
Existing medical imaging technologies struggle to provide easy-to-understand explanations of disease conditions and treatment policies to patients, as medical images captured using radiation or ultrasonic waves are unfamiliar and complex, making it difficult for doctors to grasp and explain the underlying image features to patients.
Innovation Solution
An image determination apparatus and method that utilizes machine learning to extract feature amounts from dynamic medical images, enabling diagnosis determination and generating easy-to-understand explanation data for patients based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical images are used for diagnosis and explanation, then diagnostic accuracy is improved, but patient understanding deteriorates
Solution Approach 1:
The patent creates simplified copies of medical images that retain diagnostic information while being easier for patients to understand. The system generates explanatory images and feature visualizations that are alternative representations of the original medical data, allowing doctors to use both the original images for diagnosis and the simplified copies for patient communication.
Solution Approach 2:
The patent introduces an intermediary system (the image determination apparatus) that translates complex medical image data into patient-friendly explanations. This intermediary processes the original medical images, extracts relevant features, and generates simplified visual representations and text explanations that bridge the gap between professional diagnostic data and patient comprehension.
2Loss of information
If dynamic images are used to capture temporal changes, then diagnostic information is improved, but image complexity deteriorates
Solution Approach 1:
The patent extracts key temporal features from dynamic images by identifying and isolating specific feature amounts that represent temporal changes. Instead of presenting the entire complex dynamic image sequence, the system extracts and visualizes only the relevant temporal features, such as movement patterns or changes in specific regions, making the temporal information more manageable and interpretable.
Solution Approach 2:
The patent segments the complex dynamic image data into distinct feature components that can be analyzed separately. By dividing the temporal information into discrete feature amounts (such as movement vectors, region-of-interest changes, or temporal patterns), the system makes the complex temporal data more structured and easier to process and explain.
3Measurement precision
If machine learning is used for class determination, then diagnostic accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent implements feedback loops where the machine learning model's internal feature representations are fed back into the explanation generation process. The system uses the same feature extraction mechanisms employed by the ML model to create human-readable explanations, ensuring that the explanatory information directly reflects the basis of the machine learning determination and maintains consistency between the diagnostic outcome and its justification.
Data Source
AI summary
An image determination apparatus according to an embodiment of the present disclosure includes: an image acquisition means that acquires a dynamic image obtained by capturing a site including a diagnosis target region of a patient; a feature amount extraction means that extracts a feature amount through a first process based on the dynamic image; a determination means that makes a determination related to diagnosis through a second process based on a result of machine learning based on the feature amount; and an explanation data generation means that generates explanation data based on the feature amount and the determination related to the diagnosis; and an outputter or a communicator that outputs the explanation data to outside.