Clinical Video Retrieval Using Transcript Indexing and Semantic Search
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Solution Overview
Problem
The challenge of efficiently retrieving specific information from clinical video recordings, which contain unstructured data including audio, video, and didactic materials, is hindered by the lack of effective search tools, leading to underutilization of valuable educational content.
Innovation Solution
A computer-implemented system for clinical video retrieval that includes an automatic speech recognition module for generating timestamped text transcriptions, an indexing module for creating inverted and vector indexes, a query processing module for generating dense embeddings, a retrieval module for combining keyword and semantic searches, and a reranking module for contextualized similarity scoring, to output relevant video clips based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If clinical video recordings are stored as unstructured data including audio, video, and presentation materials, then the repository can accumulate thousands of hours of educational content, but efficient retrieval of specific information becomes challenging
Solution Approach 1:
The system segments video recordings into temporal segments with timestamps and segments text content into discrete searchable units. This segmentation enables the system to manage large volumes of unstructured video data while providing efficient retrieval by breaking down the search and retrieval process into manageable segments that can be independently processed and searched.
Solution Approach 2:
The system introduces an intermediary indexing layer that converts unstructured video and audio data into structured text transcriptions and embeddings. This intermediary representation serves as a bridge between the raw unstructured content and the search query, enabling efficient retrieval without requiring direct processing of the entire video repository.
2Measurement precision
If the system processes and indexes all clinical video content to enable comprehensive search, then retrieval accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-transcribing all video content into text and pre-computing embeddings during an indexing phase. This preliminary action stores processed information in an optimized format, allowing rapid retrieval during actual search operations without requiring real-time processing of the entire video content.
Solution Approach 2:
The system creates a textual copy and embedding representation of the video content for search purposes, while the original video remains stored. This copying approach enables accurate retrieval based on text queries without requiring processing of the complete video data during each search operation, significantly reducing processing time.
3Ease of manufacture
If the system uses traditional keyword search methods, then implementation is simple, but it cannot effectively handle clinical terminology variations and contextual meanings
Solution Approach 1:
The system changes the search parameter from simple keyword matching to embedding-based semantic similarity measurement. By transforming text into dense vector embeddings that capture contextual meanings and relationships, the system can accurately handle clinical terminology variations while maintaining ease of use through a unified search interface.
Solution Approach 2:
The system replaces traditional mechanical keyword-matching algorithms with neural embedding models that understand semantic relationships. This substitution enables the system to handle clinical terminology variations and contextual meanings effectively, moving from rigid pattern matching to intelligent semantic understanding.
Data Source
AI summary
The present disclosure generally relates to an integrated approach for retrieving biomedical information from clinical video presentations. In particular, the present disclosure is directed to video retrieval systems and methods of text-video retrieval from clinical video presentations.