AI-Annotated Structured Report Regeneration for Radiology
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Solution Overview
Problem
Radiologists face challenges in generating standardized, efficient, and error-free text-based reports from medical images due to variations in reporting styles and lack of standardized templates, which is time-consuming and requires high concentration.
Innovation Solution
A system utilizing artificial intelligence algorithms to annotate medical images with vector-defined boundary tags and classification labels, enabling structured report regeneration through rule-based descriptor retrieval and user input to standardize and customize reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists manually generate text-based reports from medical images, then diagnostic accuracy is maintained, but time consumption and workload increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of report generation with an automated computer-based system that uses machine learning models to analyze medical images and generate structured reports, thereby reducing time consumption while maintaining diagnostic accuracy through algorithmic analysis
Solution Approach 2:
The system enables self-service report generation where the computer automatically analyzes images and produces reports without requiring radiologist intervention for each report, allowing radiologists to review and approve rather than manually create each report from scratch
2Adaptability or versatility
If radiologists use descriptive templates and terminologies for reporting, then customization and flexibility are achieved, but standardization and consistency deteriorate
Solution Approach 1:
The patent implements local quality by applying different levels of standardization to different parts of the report - using standardized templates and terminologies for structured sections while allowing customization in specific fields where radiologist input is needed, achieving both consistency and flexibility
Solution Approach 2:
The system dynamically adapts report generation by learning from radiologist corrections and feedback, adjusting its terminology and template usage to balance standardization requirements with the need for customized clinical descriptions based on specific case requirements
3Measurement precision
If radiologists review a large number of images for complex investigations, then comprehensive diagnosis is achieved, but concentration requirements and mental fatigue increase
Solution Approach 1:
The patent replaces the cognitive process of manual image review with automated machine learning-based image analysis that can process thousands of images systematically, maintaining comprehensive diagnostic evaluation while eliminating the mental fatigue and concentration demands on radiologists
Data Source
AI summary
A system for continually regenerating adaptive, structured, reports in association with an image, the system comprising an imaging module (IM) to output images (I), a graphical user interface dashboard (GUI) to receive output images (I); a report regeneration module (RGT), an artificial imaging module (AIM) to graphically annotate each image (I) with a vector-defined boundary tag (VT) overlaid on the displayed image (I) and with to classify each image (I) with a classification label (CL), initializing a report (RP) to be generated and to be displayed, regenerating said initial report, to cause a first regenerated report, regenerating said first regenerated report, to cause an iteratively adapted regenerated report, to be displayed on said graphical user interface dashboard, said iteratively adapted regenerated report comprising pre-defined fields to be populated based on clinical diagnoses.