Radiology Sentence Generation via Modified Word Embeddings
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Solution Overview
Problem
Existing methods for converting AI image processing outputs into natural language sentences for radiology reports are inefficient, inflexible, and require extensive manual effort, struggling to adapt to different situations and handle partial information.
Innovation Solution
A method using computer-implemented text analysis processes that generate natural language sentences by modifying word embeddings based on radiology and plain language corpora, combined with a text generator model trained on annotated data, to produce accurate and nuanced sentences from radiology image analysis outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pre-defined macros in a dictation system are used to convert radiological findings to natural language sentences, then sentence generation can be automated, but the system requires extensive manual effort to create and is not adaptable to different situations
Solution Approach 1:
The system uses pre-trained language models that automatically learn from large corpora of radiology reports, eliminating the need for manual macro creation. The model self-adjusts to different situations by processing input findings and generating appropriate sentences without human intervention.
Solution Approach 2:
The system changes from fixed macro templates to dynamic sentence generation by adjusting parameters such as input findings, anatomical locations, and abnormality types. This allows the system to adapt to different clinical situations while maintaining automation.
2Extent of automation
If pre-defined macros are used for sentence generation, then some automation is achieved, but the system cannot create narrative sentences when only partial information is provided
Solution Approach 1:
The language model acts as an intermediary that fills in missing information by learning from patterns in training data. When partial information is provided, the model infers appropriate context and generates complete, coherent sentences based on its training on radiology reports.
Solution Approach 2:
The system performs preliminary learning during the training phase on large corpora of radiology reports, storing knowledge about typical findings, anatomical structures, and sentence patterns. This preliminary action enables the model to handle partial information during actual use without requiring additional manual rules.
3Ease of operation
If AI image processing output is converted to natural language sentences, then interpretation for medical professionals is improved, but existing methods are inefficient and inflexible
Solution Approach 1:
The system replaces manual macro-creation mechanics with automated machine learning-based sentence generation. The language model automatically processes AI findings and generates natural language sentences, improving both efficiency and flexibility while maintaining ease of interpretation for medical professionals.
Data Source
AI summary
A natural language sentence is generated for a radiology report. One or more words are obtained where the one or more words were produced based on image processing of a radiology image. A computer implemented text analysis process is used to analyse the one or more words to generate a natural language sentence representing the radiology image. The natural language sentence is output. The computer implemented text analysis process includes: determining, for each of the one or more words, and using word modified embeddings, a vector representing the word; and determining, based on the determined one or more vectors, and using a text generator model, the natural language sentence.