Medical Image Interpretation Model Using Report-Derived Finding Labels
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Solution Overview
Problem
AI algorithm development for medical imaging often requires expensive, time-consuming, manual annotation of medical imaging findings.
Innovation Solution
The development of image interpretation models using neural networks trained with medical images and associated text reports, processed by a speech organization module, allowing for automatic classification of findings without manual annotation, and utilizing natural language processing to generate medical reports and diagnoses in near real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation of medical imaging findings is used to train AI algorithms, then the accuracy and reliability of the model is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary organization of text reports into structured finding items with associated text content before training the image interpretation model. This preprocessing step creates ready-to-use training data structures that eliminate the need for time-consuming manual annotation during model development
Solution Approach 2:
The system uses automatically generated and organized text reports from existing medical imaging examinations to create training data. The speech organization module and natural language processing engine self-service the data preparation task, eliminating dependency on manual radiologist annotation while maintaining model training quality
2Measurement precision
If manual annotation by radiologists is performed, then high-quality labeled training data is obtained, but the cost and resource requirements increase
Solution Approach 1:
The system copies and reuses existing text reports from medical imaging examinations as training data. Instead of creating new annotations, it extracts and organizes finding information from previously generated reports, significantly reducing the cost and resources required while maintaining data quality
Solution Approach 2:
The speech organization module and natural language processing engine serve multiple functions: they organize text reports, extract finding items, classify findings, and prepare training data. This multi-functionality eliminates the need for separate manual annotation processes, reducing overall development costs
3Reliability
If extensive manual annotation is conducted to improve model accuracy, then the model performance is enhanced, but the complexity of the development process increases
Solution Approach 1:
The system segments the text report into structured finding items with associated text content using the speech organization module. This segmentation automatically creates the training data structure needed for model training, simplifying the development process while maintaining model performance
Data Source
AI summary
An image classification model, e.g., a neural network model, may be trained on a set of training medical imaging exams each including a training report and a training medical image. A model generation module or device may, for each of the training medical imaging exams: use finding item criteria to reorganize text of the training report into a list of finding items, each associated with text extracted from the training report text, use natural language processing to analyze the resultant text associated with each finding item to determine an associated classification of each finding item, store, in a training dataset, the training medical imaging exam, the associated finding items, the matching text, and the classifications resulting from the analysis of the matching text. The trained neural network learns to output, for other medical imaging exams, one or more finding items and an associated classification data.