Endoscopy Video Timeline Interest Prediction for Faster Review
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Solution Overview
Problem
Endoscopic procedures generate large video files that are time-consuming for physicians to review manually, lacking efficient methods to navigate and identify salient portions effectively.
Innovation Solution
A system that generates an intelligent interest prediction indicator and selects key frames as thumbnails based on video analysis, allowing physicians to efficiently navigate and identify important segments using machine learning and dynamic interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually review endoscopic video recordings, then they can thoroughly examine all details, but the review process becomes time-consuming and inefficient
Solution Approach 1:
The patent introduces an artificial intelligence system as an intermediary between the endoscopic video and the physician. This AI system pre-analyzes the video content, identifies salient features, and presents them to the physician in an organized manner, thereby maintaining review accuracy while significantly reducing the time required for physicians to examine the footage
Solution Approach 2:
The system performs preliminary analysis of the endoscopic video before the physician reviews it. By automatically detecting and flagging important features, abnormalities, and key moments in advance, the system prepares the video content so that physicians can focus their attention on the most relevant segments, thus reducing overall review time without compromising accuracy
2Reliability
If the entire video recording is stored and reviewed at full quality, then all diagnostic information is preserved, but storage requirements and data processing burden increase
Solution Approach 1:
The system extracts and separates the most diagnostically relevant information from the complete endoscopic video recording. By identifying and isolating key frames, abnormal segments, and critical features, the system creates a condensed representation that preserves essential diagnostic information while reducing the overall data volume that needs to be stored and processed
Solution Approach 2:
The patent divides the continuous video recording into discrete segments based on detected features and abnormalities. Each segment is tagged and organized according to its diagnostic significance, allowing the system to store and manage video data in a structured manner that reduces redundant information while maintaining complete diagnostic coverage
Data Source
AI summary
Various techniques are described for analyzing video recordings for endoscopic and other types of medical imaging. In some examples, a system generates an intelligent interest prediction indicator displayed in association with a timeline search bar of the video recording. The intelligent interest prediction indicator is quantified and scored over the timeline based on one or more parameters. In other examples, a system intelligently selects key frames from different video segments to display as thumbnails associated with those segments. These techniques may increase the efficiency, accuracy, and speed with which a physician may review and identify salient aspects of a recorded endoscopy procedure.