Real-Time Medical Image Classification for Contrast Agent Detection
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Solution Overview
Problem
Current medical imaging technologies face challenges in accurately classifying medical images as being acquired with or without contrast agents, leading to potential errors and inefficiencies in clinical diagnostics and procedures.
Innovation Solution
A computer-implemented method using machine learning algorithms to classify medical images in real-time, determining the probability of contrast agent presence based on image processing and classification models, and integrating this classification into medical workflows for enhanced diagnostic precision and reduced contrast agent use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual discrimination of medical images by clinicians is used to determine contrast agent presence, then diagnostic judgment can be made, but inter-observer variability and potential errors increase
Solution Approach 1:
The patent replaces the manual visual inspection and subjective judgment mechanism with an automated machine learning classification system. The classifier processes medical images to determine contrast agent presence, eliminating inter-observer variability and providing consistent, objective classification results across different clinicians and settings.
2Illumination intensity
If repeated administration of contrast agent is performed to make anatomical structures visible again, then visibility is restored, but patient exposure to contrast agents increases
Solution Approach 1:
The system continuously classifies incoming medical images to detect the presence or absence of contrast agent in real-time. This feedback mechanism allows the system to identify when contrast agent is no longer present in anatomical structures, enabling timely recognition of when additional contrast administration would be necessary, thereby optimizing contrast usage and reducing patient exposure.
Solution Approach 2:
By classifying images in real-time before anatomical structures become invisible, the system provides advance warning that contrast agent is fading. This preliminary detection allows clinicians to plan subsequent imaging or contrast administration strategically, rather than reactively, reducing the need for repeated contrast doses.
3Object-affected harmful factors
If real-time classification of medical images is performed to reduce contrast agent administration, then patient safety improves, but computational processing requirements increase
Solution Approach 1:
The patent extracts and utilizes specific discriminative features from medical images that are indicative of contrast agent presence. By focusing on key visual characteristics rather than processing entire images at full resolution, the system reduces computational burden while maintaining classification accuracy, making real-time processing feasible.
Data Source
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Figure 3A~3B
AI summary
The invention relates to a computer-implemented method for real-time classification of a medical image (3) to analyse whether each of the plurality of medical images (3) was captured while a body duct (10), in particular the aortic root (11), was under the influence of a contrast agent (14), the method comprising the steps: successively receiving a plurality of medical images (3) in real-time, using an input interface, processing the plurality of medical images (3) using a processing unit (4) operatively connected to the input interface, thereby determining a probability that the medical image (3) was captured while under the influence of the contrast agent (14) based on the medical image (3) and a classification model stored on a memory unit, using the processing unit (4) for generating a classification of the medical image (3) whether the medical image was captured under the influence of the contrast agent (14) based on the probability, transmitting the classification to an output interface in real-time.