Endoscopic Image Annotation Using ML Frame Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of manually selecting and annotating images from endoscopic surgical procedures for patient reports is labor-intensive and time-consuming, often resulting in subpar information delivery due to visual disturbances and the need for detailed annotations, which burdens surgeons and may confuse patients.
Innovation Solution
Automatically apply machine learning classifiers to endoscopic images to identify anatomical features and procedural steps, generating annotations that provide context to the images, reducing the need for manual selection and annotation by surgeons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If surgeons manually select and annotate images from endoscopic video feeds for patient reports, then patients receive visual information about their procedures, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables automatic image selection and annotation through machine learning algorithms that autonomously analyze endoscopic video feeds, identify relevant frames, and generate annotations without requiring surgeon intervention. This self-service approach resolves the contradiction by eliminating manual labor while maintaining comprehensive information delivery to patients.
Solution Approach 2:
The patent replaces the mechanical process of manual image selection and annotation with an automated computational system using machine learning classifiers. This substitution transforms the labor-intensive manual process into an efficient automated workflow, significantly reducing time consumption while preserving the quality of information provided to patients.
2Loss of information
If surgeons manually annotate images to provide context for patients, then patients can understand what the images show, but the annotation process becomes even more labor-intensive than image selection
Solution Approach 1:
The machine learning system automatically generates contextual annotations by analyzing image content and generating descriptive text that explains what is shown in each image. This self-service annotation process eliminates the need for surgeons to manually write descriptions, making the process effortless while ensuring accurate contextual information is provided to patients.
Solution Approach 2:
The patent replaces the manual annotation process with an automated text generation system that uses machine learning to produce contextual descriptions. This substitution transforms the difficult manual annotation task into an automated process that is both easier to operate and maintains high quality contextual information for patient understanding.
3Measurement precision
If surgeons review hours of endoscopic footage to identify clear images, then they can select images that visualize the procedure well, but the visual disturbances from blood, turbidity, and smoke make this process complicated and time-consuming
Solution Approach 1:
The patent employs machine learning classifiers that automatically assess image quality by analyzing visual characteristics and identifying clear frames amidst visual disturbances. This computational approach replaces the manual review process, enabling rapid and precise identification of suitable images without the time-consuming effort of sifting through hours of footage obscured by blood, turbidity, and smoke.
Solution Approach 2:
The system performs self-service quality assessment by autonomously evaluating each frame's clarity and selecting the best images without surgeon intervention. This automated quality control process efficiently navigates visual disturbances and identifies clear images, resolving the contradiction between assessment precision and time consumption.
4Loss of information
If surgeons provide detailed manual annotations to ensure patient understanding, then patients receive comprehensive information, but the time required for annotation increases significantly
Solution Approach 1:
The machine learning system autonomously generates comprehensive annotations that provide complete information about procedural steps, anatomical structures, and key events. This self-service annotation process maintains information completeness while eliminating the time burden from surgeons, thereby preserving productivity.
Solution Approach 2:
The patent replaces the manual annotation process with automated text generation that produces comprehensive descriptions of procedural information. This substitution maintains the completeness of information provided to patients while dramatically improving surgeon productivity by eliminating the time-consuming manual annotation task.
Data Source
AI summary
Described herein are systems and methods for annotating one or more images generated during a surgical procedure to provide additional information to a viewer. A selection of an annotation template from a plurality of annotation templates is received. The annotation template includes a plurality of image slots. Each image slot includes one or more annotations associated with the respective image slot. Video data captured from an imaging tool configured to image an internal portion of a subject is received. The received video data is analyzed to identify an image frame of the video data to associate with an image slot of the plurality of image slots based on one or more characteristics associated with the image frame. The image frame is associated with the image slot and the one or more annotations associated with the image slot. The one or more annotations are displayed with the image frame.