Medical Device Position Detection Using ML and Fiducial Markers
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Solution Overview
Problem
Determining the normal position of a medical device in a medical image is challenging, especially for patients with complex findings, as it varies based on the type of device and anatomical position, making it difficult for doctors to assess device positioning accurately.
Innovation Solution
A method and system using machine learning models to detect and analyze medical devices in images, trained on reference images with annotations, to determine the position and presence of fiducial markers, enabling the identification of normal areas and abnormalities in medical devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a doctor manually determines the normal position of a medical device based on medical images, then the assessment can be customized to individual patient anatomy, but the process becomes time-consuming and error-prone, especially for patients with complex findings
Solution Approach 1:
The patent replaces the manual mechanical process of visual inspection and anatomical reasoning with an automated machine learning system. The ML model processes medical images and automatically determines device positioning, eliminating the time-consuming manual analysis while maintaining accuracy through trained patterns recognition.
Solution Approach 2:
The system enables self-service by automatically performing the positioning assessment without requiring doctor expertise in each specific case. The ML model independently analyzes images and provides positioning determinations, allowing the system to serve itself rather than requiring continuous human intervention for routine assessments.
2Measurement precision
If the normal position determination is customized for each patient based on anatomical variations, then accuracy improves, but the complexity of the assessment increases
Solution Approach 1:
The patent segments the complex assessment task into distinct functional components: image processing, anatomical landmark detection, device positioning detection, and abnormality determination. Each component is handled by specialized ML modules, breaking down the overall complexity into manageable sub-tasks that the system can process systematically.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw medical image and the positioning determination. It mediates the complex relationship between anatomical variations and device positioning by learning from training data, translating visual patterns into accurate positioning assessments without requiring the assessor to manually account for each anatomical variation.
3Productivity
If machine learning models are used to automatically detect medical device positioning, then assessment speed increases, but the system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive datasets of medical images with annotated positioning information. This training phase occurs before actual use, allowing the model to be pre-configured with the knowledge needed for rapid accurate assessment during deployment, separating the complexity of model training from the simplicity of execution.
Solution Approach 2:
The machine learning system provides multi-functionality by handling various tasks within the assessment workflow: image processing, anatomical landmark identification, device positioning detection, and abnormality classification. A single integrated system performs multiple functions that would otherwise require separate tools and expert knowledge, increasing productivity while consolidating complexity into one unified platform.
Data Source
AI summary
A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.