Deep Learning Radiology Annotation for Consistent Finding Comparison
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Solution Overview
Problem
Current radiology workflows are inefficient, requiring numerous interactions and leading to inconsistencies in measurements and findings, making it difficult to compare results between studies and radiologists.
Innovation Solution
A system and method for automated annotation of radiology findings using convolutional neural networks and deep learning algorithms to minimize interactions and standardize the analysis process, enabling consistent comparison and display of abnormal findings with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated annotation using deep learning algorithms is implemented, then productivity and consistency are improved, but device complexity increases
Solution Approach 1:
A radiology assistant system acts as an intermediary between radiologists and imaging studies. The assistant receives imaging studies, automatically generates annotations using deep learning algorithms, and presents them to radiologists for review. This intermediary layer automates routine annotation tasks while maintaining radiologist oversight, thereby improving productivity without completely replacing human expertise.
Solution Approach 2:
The system enables self-service annotation by allowing radiologists to review and accept automatically generated annotations with minimal interaction. The deep learning model performs the heavy lifting of initial annotation, and radiologists can quickly verify or reject results, significantly reducing the time and effort required compared to manual annotation from scratch.
2Measurement precision
If manual annotation by radiologists is used, then measurement precision can be achieved, but loss of time increases
Solution Approach 1:
The deep learning model performs preliminary annotation before radiologist review. It pre-processes imaging studies and generates initial measurements and findings, which radiologists then verify and refine if necessary. This preliminary action by the AI system eliminates the need for radiologists to perform time-consuming manual measurements from scratch, significantly reducing annotation time while maintaining precision through human verification.
3Measurement precision
If standardized annotation processes are implemented, then measurement precision and consistency are improved, but ease of operation decreases
Solution Approach 1:
The system enforces standardized annotation processes automatically through the deep learning model, which applies consistent measurement criteria and annotation formats across all studies. Radiologists interact with a simplified interface that presents pre-standardized annotations for review, eliminating the need for them to manually adhere to complex standardization protocols while still achieving consistent, precise measurements.
Data Source
AI summary
A method for the automated annotation of radiology findings includes: receiving a set of inputs, determining a set of outputs based on the set of inputs, assigning labels to the set of inputs, and annotating the set of inputs based on the labels. Additionally, the method can include any or all of: presenting annotated inputs to a user, comparing multiple sets of inputs, transmitting a set of outputs to a radiologist report, or any other suitable processes.