Medical Image Foreign Object Detection for Surgical Extraction Planning
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Solution Overview
Problem
Surgical procedures face complications due to undetected foreign objects, leading to improvised removal methods that can damage surgical sites and prolong surgery duration and cost.
Innovation Solution
A computer-implemented method using an object-detection algorithm to identify foreign objects in medical images, determine their pose, and generate an extraction plan to safely remove them, integrating with a surgical system for automated guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual improvised removal of foreign objects is performed during surgery, then the foreign object can be removed, but the surgical plan may be compromised, causing damage to implant receiving surfaces or sensitive structures
Solution Approach 1:
The system performs preliminary detection of foreign objects using preoperative imaging (CT, MRI, X-ray) and automatically generates extraction plans before surgery begins. This advance planning allows the surgical team to understand the foreign object's location, composition, and relationship to surrounding structures, enabling safe removal without compromising the surgical plan or damaging sensitive structures.
Solution Approach 2:
The system introduces an intermediary computational layer that processes medical images, detects foreign objects, and generates extraction plans. This intermediary system acts as a bridge between the foreign object detection need and the surgical plan execution, providing automated guidance that protects the surgical plan integrity while facilitating foreign object removal.
2Loss of information
If foreign objects are discovered intraoperatively, then the surgeon becomes aware of the foreign object, but surgery duration is prolonged and additional costs are incurred
Solution Approach 1:
The system performs foreign object detection during the preoperative planning phase using existing imaging data, eliminating the need for intraoperative discovery. The automated extraction plan is generated before surgery begins, ensuring the foreign object is identified and accounted for in the surgical plan, thereby preventing unexpected delays during the procedure.
3Measurement precision
If automated object-detection algorithm is used to identify foreign objects, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the object-detection algorithm automatically processes medical images, identifies foreign objects, and generates extraction plans without requiring manual intervention. The system self-calibrates and self-executes the detection and planning process, improving accuracy while managing complexity through automation rather than human-operated complex systems.
Solution Approach 2:
The system replaces manual mechanical inspection methods with automated computational algorithms for foreign object detection. Instead of relying on surgeon visual inspection or complex manual imaging analysis, the system uses software-based object-detection algorithms that process medical images automatically, reducing the need for complex mechanical or manual detection systems.
Data Source
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AI summary
Computer-implemented methods, systems, software, and techniques involve using an object-detection algorithm to detect a foreign object in a medical image of a target anatomy. The foreign object is non-native to the target anatomy. The pose of the foreign object in the medical image and a surgical plan is associated with the medical image. An interaction between the foreign object and the surgical plan is determined. Based on the interaction, an extraction plan is generated for removing the foreign object from the target anatomy.