Medical Device Image Monitoring With 3D Pose Inference
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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 provide real-time views of medical devices, then practitioners can obtain real-time guidance, but the position and orientation representation of invasive medical devices is inadequate
Solution Approach 1:
The patent applies dimensionality change by inferring three-dimensional position and orientation information from two-dimensional fluoroscopic images. The system uses machine learning models trained on annotated images to predict depth, distance, and angular orientation that are not directly visible in 2D projections, effectively adding the missing spatial dimension back into the imaging data.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the 2D imaging system and the practitioner's need for 3D spatial understanding. This model processes the 2D images and generates inferred 3D position and orientation data, serving as a bridge that translates limited 2D information into comprehensive spatial awareness without requiring additional 3D imaging hardware.
2Productivity
If practitioners manually monitor device position using 2D images, then they can perform procedures, but procedure duration increases
Solution Approach 1:
The system implements self-service by automatically inferring 3D position and orientation data from 2D images without requiring manual measurement or interpretation by practitioners. The machine learning model continuously processes imaging data and provides real-time spatial information, allowing the system to serve itself in extracting spatial intelligence rather than relying on human operators to manually assess device positioning.
Solution Approach 2:
The patent replaces the mechanical system of manual visual assessment and physical measurement with an automated computational system. Instead of practitioners manually analyzing 2D images to estimate device position and orientation, the system uses trained neural networks to automatically perform these measurements, substituting human cognitive and manual processes with automated image processing and machine learning inference.
3Object-affected harmful factors
If extended procedure duration occurs due to inadequate imaging, then procedures can be completed, but radiation exposure to patient and operator increases
Solution Approach 1:
The system implements feedback by continuously analyzing fluoroscopic images and providing real-time inferred 3D position and orientation information back to the practitioner. This closed-loop feedback allows practitioners to immediately adjust device positioning based on accurate spatial data, reducing trial-and-error maneuvers and minimizing the time required to achieve correct device placement, thereby reducing overall radiation exposure.
Data Source
AI summary
A system and method for image-based monitoring of an object inserted into a patient is disclosed. A model can be trained for an object configured to be inserted into a patient as part of a medical procedure, the trained model being generated from one or more machine learning algorithms that are trained on annotated images of the object with spatial information of the object. An imaging computer system can receive one or more images of the object inserted within the patient 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 spatial information of the object within the patient. A display can output the one or more images and the current spatial information of the object.