Automated Medical Image Triage via Cognitive Classification
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Solution Overview
Problem
The manual review and classification of medical images by radiologists is inefficient and prone to delays and errors, leading to increased stress for patients and wastage of medical resources, as the volume of images grows beyond the capacity of experts to analyze quickly and accurately.
Innovation Solution
An automated system using a cognitive system with machine learning and computer vision techniques to classify medical images, generate worklists, prioritize tasks, reserve medical resources, and provide differential diagnoses, thereby streamlining the triaging process and reducing the workload on radiologists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and classification by radiologists is used, then diagnostic accuracy can be maintained, but the process becomes inefficient and prone to delays
Solution Approach 1:
The patent introduces an automated classification system as an intermediary between image acquisition and radiologist review. This system pre-processes and categorizes images based on multiple features (image quality, anatomical landmarks, pathology indicators), allowing radiologists to focus their expertise on complex cases while routine images are handled automatically, thus maintaining accuracy while improving efficiency
Solution Approach 2:
The system performs preliminary classification and triage of medical images before they reach the radiologist. By automatically evaluating image quality, detecting anatomical structures, and identifying potential pathologies in advance, the system prepares images for optimal review, reducing the time radiologists need to spend on initial assessment while maintaining diagnostic accuracy
2Quantity of substance
If the volume of medical images increases due to technology enhancements, then more comprehensive patient data is available, but the capacity of radiologists to analyze each image is exceeded
Solution Approach 1:
The patent segments the large volume of medical images into manageable categories based on multiple criteria including image quality, anatomical region, pathology type, and urgency indicators. This segmentation allows the system to prioritize and route different image types to appropriate review pathways, enabling radiologists to handle increased volumes by focusing on high-priority cases while automated systems manage routine classifications
Solution Approach 2:
The automated classification system performs multiple functions simultaneously: quality assessment, anatomical landmark detection, pathology identification, and triage prioritization. This multi-functional approach allows the system to process diverse image types (CT, MRI, X-ray, ultrasound) through a unified platform, scaling efficiently to handle increasing image volumes without proportionally increasing radiologist workload
3Measurement precision
If manual classification systems are used, then detailed diagnostic evaluation can be performed, but the process delays patient diagnosis and increases stress
Solution Approach 1:
The system implements continuous automated classification and monitoring of medical images as they are acquired and stored. Rather than batch processing, the system continuously evaluates new images, updates classifications in real-time, and immediately notifies relevant personnel of critical findings, eliminating delays associated with manual review scheduling while maintaining precise classification through persistent automated analysis
4Adaptability or versatility
If radiologists manually access multiple systems for follow-up treatments, then comprehensive patient care coordination is possible, but the process becomes complex and time-consuming
Solution Approach 1:
The patent merges the classification system with the hospital information system and resource allocation system into an integrated platform. This integration allows automatic generation of follow-up treatment recommendations, automated scheduling of appointments, and coordinated resource allocation based on classification results, eliminating the need for radiologists to manually access multiple systems while maintaining comprehensive care coordination
Data Source
AI summary
Methods and systems for automatically triaging an image study of a patient generated as part of a medical imaging procedure. One system comprises a computing device including an electronic processor. The electronic processor is configured to receive, from a cognitive system applying a model developed using computer vision and machine learning techniques based on deep learning methodology to classify image studies, a classification assigned to the image study using the model, automatically generate a differential diagnosis for the patient based on the classification assigned by the model, and automatically adjust triaging of the image study based on the differential diagnosis.