Internal Medical Device 3D Position and Orientation Detection
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Solution Overview
Problem
Existing 2D medical imaging technologies inadequately represent the position and orientation of invasive medical devices, leading to increased procedure duration and radiation exposure for patients and operators.
Innovation Solution
A system utilizing machine learning algorithms to infer 3D position and orientation of medical devices from 2D images, enhancing existing imaging devices with 3D information without additional hardware, by training models on annotated images to predict device orientation and position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D imaging technologies are used to visualize medical devices, then practitioners can obtain real-time guidance, but the position and orientation information is inadequate
Solution Approach 1:
The patent applies dimensionality change by inferring three-dimensional position and orientation information from two-dimensional imaging data. Machine learning models process 2D images and predict 3D spatial coordinates (x, y, z) and orientation angles (roll, pitch, yaw), effectively adding depth and spatial context that is not directly visible in the 2D images alone.
2Productivity
If practitioners manually determine device position from 2D images, then no additional hardware is needed, but procedure duration increases
Solution Approach 1:
The patent replaces manual mechanical assessment with automated machine learning-based computational analysis. Instead of practitioners visually estimating device position and orientation from 2D images, ML models automatically perform the spatial inference, significantly reducing the time required while maintaining or improving accuracy.
3Object-affected harmful factors
If procedure duration is reduced, then radiation exposure decreases, but measurement accuracy may be compromised
Solution Approach 1:
The patent performs preliminary spatial inference using machine learning models before and during the procedure. By pre-training models on annotated images and then applying them to predict device position and orientation in real-time, the system provides accurate measurements quickly, allowing reduced radiation exposure while maintaining precision.
Data Source
AI summary
A system, method, and computer program product for image-based detection of conditions for an object internal to a patient is disclosed. A model can be trained for an internal object, such as an invasive medical device, the trained model being generated from one or more machine learning algorithms that are trained on annotated images of the object with condition information of the object. An imaging computer system can receive one or more images of the internal object captured by an imaging device positioned external to the patient. The imaging computer system can further determine, based on applying the trained model to the one or more images of the object, current condition information for the internal object, such as current spatial information for the object. A display can output the one or more images and the condition information for the object.