AI-Assisted X-Ray 3D Localization for Calibration-Free Surgery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing surgical procedures for implanting bone stabilization devices require reliable measurement of dimensions and 3D positioning based on X-ray images, which are challenging due to the need for calibration and the use of reference bodies, and often necessitate multiple image acquisitions.
Innovation Solution
A system utilizing artificial intelligence, specifically deep learning and neural networks, to determine the 3D representation and localization of objects in X-ray images without the need for reference bodies, allowing for precise measurement and reconstruction of anatomical structures and implants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference bodies are used for calibration and 3D positioning in X-ray imaging, then measurement precision and 3D localization accuracy are improved, but device complexity and surgical procedure time increase due to additional calibration steps and multiple image acquisitions
Solution Approach 1:
The patent extracts and removes the reference body from the surgical workflow, replacing it with AI-based direct analysis of anatomical structures in standard X-ray images. This eliminates the need for separate calibration objects while maintaining measurement precision through neural network-based 3D reconstruction of bone anatomy.
Solution Approach 2:
The patent replaces the mechanical/calibration-based system (reference bodies, physical measurement tools) with an AI-based digital system that uses deep learning models to directly infer 3D positions, orientations, and dimensions from 2D X-ray images, thereby simplifying the overall system complexity.
2Measurement precision
If multiple X-ray images are acquired from different directions for 3D reconstruction, then 3D representation accuracy is improved, but loss of time and productivity decrease due to repeated image acquisitions
Solution Approach 1:
The patent applies preliminary action by pre-training AI models on extensive 3D anatomical data and pre-establishing 3D bone models that can be rapidly matched to 2D X-ray images during surgery. This allows accurate 3D reconstruction from a single或少量 images without requiring multiple acquisitions from different directions.
Solution Approach 2:
The patent changes the approach from acquiring multiple images with different geometric parameters (multiple angles, positions) to using a single image with AI-based parameter inference, where the neural network directly predicts 3D parameters (position, orientation, dimensions) from the 2D projection.
3Reliability
If reference bodies are mounted on X-ray imaging devices and instrumentation for calibration, then reliability of measurements is improved, but ease of operation deteriorates due to additional setup and calibration steps
Solution Approach 1:
The patent implements self-service by enabling the X-ray imaging system to automatically perform calibration and 3D localization functions through AI algorithms that directly analyze anatomical structures in the images. The system self-calibrates by recognizing bone anatomy and deriving measurement references from the patient's own anatomy rather than requiring external reference bodies.
Data Source
AI summary
Supported by artificial intelligence, an object is classified in an X-ray projection image. A 3D representation as well as a localization of the classified object can be determined by matching a model of the classified object to a visualization of the classified object in the X-ray image.