AI Report Validation for X-ray Anomaly Detection
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Solution Overview
Problem
Conventional AI-based systems for X-ray imaging are costly and inefficient in detecting and classifying anomalies, often leading to inappropriate highlighting of regions and complex algorithm structures, which can result in biased or inaccurate radiological reports.
Innovation Solution
A system and method utilizing an AI-based image classification model that tokenizes and classifies textual outputs from a report generating model into predetermined categories, comparing them with actual inferences to determine a match score and validate the report, thereby improving the accuracy and efficiency of anomaly detection and classification in digital X-ray radiographs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CNN models are used for anomaly detection, then detection capability is achieved, but the system becomes costly and complex with inappropriate highlighting
Solution Approach 1:
The patent segments the anomaly detection process into distinct modules: a CNN-based image processing model for feature extraction, a separate report generating model for text output, and a validation model for verification. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent introduces a validation model as an intermediary component that bridges the gap between the report generating model and the final output. This intermediary validates the generated reports against ground truth data, improving reliability without requiring the main detection system to be overly complex.
2Productivity
If conventional AI models are used for report generation, then automated reporting is achieved, but biases and clerical errors increase
Solution Approach 1:
The patent implements a feedback mechanism where the validation model receives ground truth data and compares it with the generated report output. This feedback loop allows the system to identify and correct biases and errors, improving report accuracy while maintaining automated generation efficiency.
Solution Approach 2:
The patent replaces manual report verification with an automated validation model that uses machine learning to assess report accuracy. This substitution eliminates clerical errors associated with manual review while maintaining high productivity through automated processing.
3Measurement precision
If detailed anomaly analysis is performed, then detection precision is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary anomaly detection and classification using the CNN model before detailed analysis. This preliminary action identifies potential anomalies and their basic characteristics, allowing the system to focus computational resources only on cases requiring detailed analysis, thus reducing overall processing time while maintaining precision.
Data Source
AI summary
Method and system for validating a report generated by a report generating model is provided. The method includes receiving an output that corresponds to an anomaly detected in an image from report generating model and includes one textual sentence. The method includes receiving an actual inference corresponding to anomaly and includes one textual sentence. The method further includes tokenizing output to generate output tokens and tokenizing actual inference to generate inference tokens. The method further includes classifying output tokens into predetermined categories. The method further includes classifying inference tokens into predetermined categories. The method further includes comparing output tokens with a corresponding inference tokens and assigning a match score to output tokens. The method further includes determining a combined score for the output based on the match score and validating the output based on the combined score.