AI Imaging Scan Annotation for Surgical Guidance
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Solution Overview
Problem
Interpreting and annotating medical imaging scans, such as X-rays, CT scans, or MRI scans, is time-consuming and requires significant expertise, especially for complex surgical procedures, leading to inefficiencies and potential inaccuracies.
Innovation Solution
A computerized method and system using machine learning algorithms to annotate imaging scans, providing graphical tools for users to identify and align anatomical structures, with AI-driven identification and annotation tools for precise image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of imaging scans is performed by healthcare professionals, then accuracy of anatomical identification is improved, but time consumption and workload increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising machine learning models and image processing algorithms that act as a mediator between the raw imaging scan and the final annotation. This intermediary automatically identifies anatomical structures and generates preliminary annotations, which then require minimal human verification rather than complete manual annotation, thus resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The system enables self-service annotation by allowing the imaging scan itself to provide the annotation information through automated analysis. The machine learning models extract anatomical features directly from the scan data without requiring external expert interpretation for every detail, reducing the burden on healthcare professionals while maintaining accuracy through automated detection
2Reliability
If expert interpretation of imaging scans is performed manually, then diagnostic accuracy is improved, but resource consumption and cost increase
Solution Approach 1:
The patent segments the diagnostic process into multiple components: automated image processing, machine learning-based anatomical identification, preliminary annotation generation, and expert verification. This segmentation allows routine tasks to be handled by automated systems while experts focus only on complex cases requiring human judgment, improving resource efficiency without sacrificing diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical system of manual expert interpretation with an automated computational system using machine learning and image processing algorithms. This substitution handles routine annotation tasks automatically, freeing up expert resources for more complex diagnostic challenges and improving overall productivity while maintaining reliability through the automated system's consistent performance
3Manufacturing precision
If detailed annotation of complex surgical procedures is performed manually, then surgical precision is improved, but the complexity and time required for preparation increase
Solution Approach 1:
The patent applies preliminary action by performing automated annotation and surgical planning preparation before the actual surgical procedure. The system pre-identifies anatomical structures, pre-maps surgical pathways, and pre-annotations critical areas on the imaging scans, so that when the surgeon performs the procedure, the precision-enhancing work has already been completed, reducing intraoperative complexity and time requirements
Data Source
AI summary
A computerized method and system for annotating imaging scans for use in surgery. The method involves receiving an image that contains a visual representation of an area of a patient's body from a communication device. The image is displayed via a graphical user interface on a computing device that provides a set of graphical tools to a user. The tools allow the user to annotate the image, which can be based at least in part on input to the graphical user interface. An annotation is received relating to one or more portions of the anatomy that appears in the image and is then applied to the image so that it aligns with the relevant anatomy. The method and system can further include artificial intelligence or machine learning modules to generate annotations and identify anatomical features and potential medical defects in the image. The image and annotations can then be used to guide surgical procedures.