Endoscopic Image Classification for Automatic Procedure Annotations
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Solution Overview
Problem
The process of providing patients with clear and informative images from endoscopic surgical procedures is labor-intensive for surgeons, as they must sift through hours of video footage to select and manually annotate images, which can be time-consuming and ineffective in conveying procedural information to laypeople.
Innovation Solution
Automatically apply machine learning classifiers to endoscopic images during surgical procedures to identify anatomical features and procedural steps, generating annotations that are overlaid on the images to provide context, reducing the need for manual selection and annotation by surgeons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgeons manually review and annotate endoscopic images, then clear and informative visual reports can be provided to patients, but the process becomes extremely labor-intensive and time-consuming
Solution Approach 1:
The system enables self-service by allowing the endoscopic imaging system to automatically generate annotated images with procedural information without requiring surgeon intervention. The imaging system captures video data, converts it to image frames, applies machine learning classifiers to identify anatomical features and procedural steps, and automatically generates annotations that are displayed with the images, eliminating the need for manual annotation while maintaining high information quality for patients
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Machine learning classifiers substitute for the surgeon's manual review and annotation work, automatically identifying anatomical structures and procedural steps in endoscopic images and generating contextual annotations without physical human intervention in the image processing workflow
2Reliability
If surgeons spend extensive time reviewing hours of video footage to select clear images, then high-quality images can be provided to patients, but significant time is lost in the process
Solution Approach 1:
The system performs preliminary action by automatically processing and evaluating all video frames during or immediately after the procedure. Machine learning classifiers pre-analyze anatomical features, procedural steps, and image quality metrics for all frames, identifying suitable images and generating annotations in advance before patient consultation, eliminating the need for time-consuming manual review sessions
Solution Approach 2:
The automated machine learning-based image selection system replaces the manual mechanical process of surgeon review. The system automatically evaluates video frames based on multiple criteria including anatomical feature presence, procedural step relevance, and image quality metrics, rapidly selecting appropriate images without the time investment required for manual surgeon review
3Loss of information
If manual annotations are added to help patients understand images, then contextual information is provided, but the annotation process itself becomes additional labor-intensive work
Solution Approach 1:
The system provides self-service annotation by automatically generating contextual information about anatomical features and procedural steps using machine learning classifiers. The annotations are created without surgeon intervention, extracting relevant information directly from the video data and image content, and displaying them alongside the selected images to enhance patient understanding without adding to surgeon workload
Solution Approach 2:
The patent replaces the manual mechanical annotation process with automated computational annotation. Machine learning classifiers substitute for surgeon annotation work, automatically identifying and labeling anatomical structures, procedural steps, and key features in the images, generating contextual information that aids patient understanding without requiring manual surgeon time investment
Data Source
AI summary
A method for automatically generating and applying annotations to one or more images captured during a surgical procedure using an imaging tool are provided. In one or more examples, the annotations can be generated by applying one or more machine learning classifiers to the images to determine the presence of various features contained within the images. Optionally, the machine learning classifiers can be configured to determine the anatomy displayed in a particular image as well as the procedure step shown in a given image. Using these two determinations, the systems and methods described herein can generate one or more annotations that are then overlaid on or laid next to an image so as to provide the patient or other person viewing the image with context as to what the image is showing.