ML-Based Report Section Mapping for Medical Imaging Review
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Solution Overview
Problem
Medical imaging exam reviewers face fatigue and cognitive biases due to the division of attention between medical images and electronic reports, leading to perceptual errors, as they must frequently switch focus between the two.
Innovation Solution
A system using machine learning to automatically associate user input with sections of an electronic report, generating mappings from prior reports to automatically insert text into the appropriate sections, reducing the need for reviewers to manually select placement within the report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the report is displayed in a separate window or on a separate display device than the images, then the reviewer can access both images and report simultaneously, but the reviewer must frequently switch attention between images and report, causing fatigue and perceptual errors
Solution Approach 1:
The patent combines the report display with the image display by inserting report sections directly into the image viewer interface. This merging eliminates the need for separate windows or display devices, allowing reviewers to access both images and report in a unified interface, thereby reducing attention switching while maintaining simultaneous accessibility.
Solution Approach 2:
The patent introduces an automated text insertion system as an intermediary that bridges the gap between image analysis and report generation. This intermediary automatically captures reviewer input and places it in the appropriate report sections, reducing the cognitive load and attention switching required when manually managing both image viewing and report composition.
2Measurement precision
If the reviewer manually selects and inserts text into report sections, then the report can be accurately populated with findings, but the process requires frequent attention switching and manual effort, increasing fatigue and time consumption
Solution Approach 1:
The patent implements preliminary action by pre-organizing report sections and pre-positioning text insertion points within the image viewer interface. Report templates are prepared in advance with designated areas for different types of findings, allowing the reviewer to simply input text without needing to manually navigate or select where it should be placed, thereby reducing time and effort.
Solution Approach 2:
The system provides self-service functionality by automatically managing the text insertion process. Once the reviewer provides input, the system autonomously identifies the appropriate report section and inserts the text without requiring manual selection or intervention, thereby eliminating the time-consuming manual effort while maintaining accurate report population.
3Adaptability or versatility
If the report interface includes multiple sections and input fields, then the report can be comprehensively structured, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent applies local quality by providing different levels of interface complexity in different regions. The main image viewing area remains simple and uncluttered, while the report sections are inserted directly into this area with minimal additional interface elements. This allows comprehensive report structure to be maintained while keeping the overall interface complexity low, as each local area serves its specific function without requiring the user to navigate a complex global interface.
Data Source
AI summary
Systems and methods for generating electronic reports for medical images. One system includes an electronic processor configured to access a plurality of prior reports associated with a user and automatically generate a mapping using machine learning based on the plurality of prior reports. The mapping associates language included in the plurality of prior reports with at least one section of a report. The system stores the mapping to a memory, receives input from the user for an electronic report associated with a medical image, accesses the mapping from the memory, automatically determines a section in the electronic report associated with the input based on the mapping, and automatically inserts text into the section in the electronic report based on the input.