Medical Image Visualization Workflow for Multi-Lesion Reading
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Solution Overview
Problem
Existing medical imaging technologies struggle to efficiently assist radiologists in identifying various types of lesions in a series of medical images, leading to increased reading time and reduced workflow efficiency, as they often require manual selection and adjustment of display settings based on limited diagnostic information.
Innovation Solution
A computing system utilizing artificial neural networks to generate representative visualization formats for medical images, allowing users to approve or reject analysis results and enabling independent reprocessing, thereby optimizing the display and analysis workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CAD technology is used to assist lesion diagnosis, then doctors can receive automated analysis support, but the system is limited to significantly restricted areas and cannot handle multiple lesion types effectively
Solution Approach 1:
The patent implements a universal lesion detection system that can identify multiple types of lesions including lung nodules, ground glass opacities, consolidations, and other abnormalities within a single integrated framework. The system uses a unified deep learning architecture that processes various lesion types simultaneously, eliminating the need for separate specialized systems for each lesion type while maintaining high detection accuracy across all categories.
2Loss of information
If multiple lesions are detected in a series of medical images, then comprehensive diagnostic information is provided, but radiologists face difficulty in efficiently selecting and prioritizing lesions for review
Solution Approach 1:
The system performs preliminary prioritization and organization of detected lesions before presentation to the radiologist. It automatically ranks lesions based on multiple criteria including detection confidence scores, lesion characteristics, and clinical relevance, pre-organizing them in order of diagnostic importance. This preliminary sorting action reduces the time radiologists need to spend evaluating and prioritizing lesions, as the most critical cases are already positioned at the top of the review queue.
3Productivity
If AI analysis results are provided for medical images, then reading efficiency is improved, but clinicians need the ability to verify and override AI decisions for critical patient care
Solution Approach 1:
The system implements a feedback mechanism where radiologist decisions (confirmations, rejections, or modifications of AI findings) are fed back into the system to refine future AI predictions. The interface provides clear indicators of AI confidence levels and allows radiologists to easily override AI decisions when clinically warranted. This feedback loop maintains workflow simplicity while ensuring clinical accuracy, as the system adapts to radiologist preferences and clinical judgment over time.
Data Source
AI summary
Disclosed is an apparatus for assisting the reading of a medical image based on a medical artificial neural network, the apparatus including a computing system. An apparatus according to an embodiment of the present invention includes a computing system, and the computing system includes at least one processor. The at least one processor acquires or receives a first analysis result obtained through the inference of a first artificial neural network for a first medical image, generates a first visualization format, which is a representative visualization format of the first medical image, based on the first analysis result, and visualizes the first medical image and the first analysis result based on the first visualization format.