Natural Language Report Reference Detection and Image Association
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Solution Overview
Problem
Radiologists face challenges in efficiently accessing and linking referenced medical image data within free-text reports, which can be time-consuming due to the unstructured nature of the reports and the need to manually look up image data.
Innovation Solution
A system that uses natural language processing to detect and interpret references to data objects within textual reports, allowing for automatic association and retrieval of relevant data objects, enabling the creation of hyperlinks or snapshots for easy access to image data, and re-creating views based on textual descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If free-text reports with natural language are used to document medical images, then the reports can be written flexibly and comprehensively, but it becomes time-consuming to look up referenced image data
Solution Approach 1:
The system performs preliminary action by automatically detecting and extracting image references from the free-text report during the report creation process itself. The reference detector identifies image identifiers, series information, and slice numbers as the radiologist writes the report, and the associating unit immediately links these references to the corresponding image data in the database, so that when the report is later viewed, the images are already accessible without manual lookup.
2Reliability
If manual lookup of referenced image data is required, then data accuracy can be verified, but workflow efficiency decreases
Solution Approach 1:
The system implements self-service by automatically performing the reference detection and image association tasks that would otherwise require manual verification. The reference detector autonomously identifies image references in the report text, and the associating unit automatically links them to the correct image data, eliminating the need for manual lookup while maintaining data accuracy through automated verification processes.
3Adaptability or versatility
If unstructured natural language reports are processed, then clinical flexibility is maintained, but automated data retrieval becomes difficult
Solution Approach 1:
The system introduces an intermediary layer consisting of the reference detector and associating unit that bridges the unstructured natural language report and the structured image database. The reference detector acts as an intermediary by parsing the free-text report to identify image references, and the associating unit serves as an intermediary by creating links between the textual references and the actual image data, enabling automated retrieval without requiring the report itself to be structured.
Data Source
AI summary
A system for processing a report, comprising a natural language processing unit (1) for processing a natural language textual report to detect a description of a reference to at least part of a data object, wherein the description is expressed in natural language as a part of the natural language textual report. The system comprises an accessing unit (2) for accessing said at least part of the data object in a collection of data objects, based on the reference. The system comprises an associating unit (3) for associating the accessed at least part of the data object with the description of the reference. The natural language processing unit (1) comprises a view parameter extraction unit (4) for extracting a view parameter indicative of a view of the data object from the description of the reference.