Machine Learning Medical Image Analysis with Graphical Reporting
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Solution Overview
Problem
The rapid growth of medical images from advanced imaging technologies overwhelms physicians, necessitating improved speed and accuracy in medical image review, integration of clinical and reference data, and automation of diagnostic processes.
Innovation Solution
The development of systems and methods using machine learning with graphical reporting to train a learning engine, which analyzes medical images and associated data to provide diagnostic information, categorize images, and automate tasks such as triaging and generating reports, leveraging clinical, demographic, and external data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually review medical images, then diagnostic accuracy is maintained, but the time required and workload increase significantly
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the medical images and the physician. This system pre-analyzes images, identifies abnormalities, and prioritizes cases for review, allowing physicians to focus their expertise on critical cases while maintaining diagnostic accuracy and reducing overall review time
Solution Approach 2:
The system performs preliminary analysis of medical images before physician review, automatically detecting anomalies, categorizing images by urgency, and preparing prioritized lists for physician examination. This preliminary processing reduces the time physicians need to spend on routine screening while maintaining accuracy
2Loss of information
If more medical images are acquired using advanced imaging technologies, then diagnostic information increases, but the capacity of physicians to analyze each image individually is overwhelmed
Solution Approach 1:
The system extracts and prioritizes only the most critical images and findings from large datasets, separating urgent cases requiring physician attention from routine cases. This extraction approach allows physicians to focus on high-value diagnostic information while the system handles volume management
Solution Approach 2:
The patent replaces the mechanical process of manual image-by-image review with an automated computational system that can process large volumes of images simultaneously. This substitution enables the system to handle increased imaging throughput while maintaining analysis quality
3Loss of time
If graphical reporting with structured data is used to train the learning engine, then the speed of learning image characteristics increases, but the complexity of the system increases
Solution Approach 1:
The system transforms unstructured medical image data into structured graphical representations with standardized parameters and features. This parameterization enables efficient machine learning training while maintaining manageable system complexity through consistent data formatting and feature extraction protocols
Data Source
AI summary
Methods and systems for performing image analytics using graphical reporting associated with clinical images. One system includes at least one data source and a server. The server includes an electronic processor and an interface for communicating with the data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. Each graphical reporting includes a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information. The model is used to automatically analyze an image.