AI Hanging Protocol for Medical Image Reading Assistant
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Solution Overview
Problem
Current medical imaging technologies face inefficiencies in diagnosing multiple types of lesions within a single medical image series, as clinicians and radiologists spend excessive time selecting and displaying lesions, leading to prolonged reading times and decreased workflow efficiency.
Innovation Solution
A medical image reading assistant apparatus utilizing a computing system with a processor that generates and executes display settings based on analysis results from a medical artificial neural network, providing hanging protocols tailored to each type of lesion and disease, allowing for optimized display layouts and user interfaces that prioritize diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CAD technology is used to assist diagnosis, then lesion detection capability is improved, but reading efficiency deteriorates due to the need to manually select and evaluate multiple nodules
Solution Approach 1:
The system performs preliminary action by automatically evaluating all detected nodules and pre-sorting them by malignancy probability before the radiologist needs to review them. The artificial neural network analyzes each nodule's features and assigns a malignancy score, so that when the radiologist opens the case, the nodules are already organized in priority order, eliminating the need for manual evaluation of all nodules.
Solution Approach 2:
The system provides self-service by automatically generating the hanging protocol display settings based on the detected lesions and their malignancy probabilities. Instead of requiring the radiologist to manually configure display parameters for each nodule, the system self-adjusts the display to show the most suspicious nodules first, with appropriate windowing and labeling already configured.
2Adaptability or versatility
If multiple types of lesions are detected in a single medical image series, then diagnostic comprehensiveness is improved, but workflow complexity increases due to the need to manage multiple lesion types
Solution Approach 1:
The system applies segmentation by separating different lesion types into distinct evaluation categories. The artificial neural network identifies whether each detected nodule represents a solid tumor, ground glass nodule, or other lesion type, and the hanging protocol then applies different display settings and evaluation workflows for each lesion category, making the complex multi-lesion situation manageable through structured organization.
Solution Approach 2:
The system achieves universality by creating a single integrated workflow that can handle multiple lesion types simultaneously. The hanging protocol framework accommodates different lesion characteristics (solid, ground glass, size, location) within one unified display interface, allowing the radiologist to review all lesion types using the same workflow structure rather than requiring separate protocols for each lesion type.
3Ease of operation
If manual selection and display of lesions is performed, then control over display settings is improved, but reading time increases significantly
Solution Approach 1:
The system performs preliminary action by automatically evaluating all detected nodules and pre-sorting them by malignancy probability before the radiologist needs to review them. The artificial neural network analyzes each nodule's features and assigns a malignancy score, so that when the radiologist opens the case, the nodules are already organized in priority order, eliminating the need for manual evaluation of all nodules.
Solution Approach 2:
The system applies parameter changes by automatically adjusting display parameters such as window level, window width, and contrast enhancement based on the detected lesion characteristics. The hanging protocol modifies these parameters dynamically to optimize the display for each lesion type, providing the radiologist with pre-optimized images rather than requiring manual parameter adjustment.
Data Source
AI summary
Disclosed herein is a medical image reading assistant apparatus that provides hanging protocols based on a medical artificial neural network. The medical image reading assistant apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to generate a first display setting based on the first analysis result, and to execute the first display setting so that the first medical image and the first analysis result are displayed on a screen based on the first display setting.