Automated Medical Image Pipeline Selection via ML Analysis
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Solution Overview
Problem
Current medical image analysis tools are limited to specific medical imaging technologies and require user specification, leading to potential incorrect or non-optimal algorithm application, and lack automation in determining appropriate analytics for diverse medical image characteristics.
Innovation Solution
An automated medical image processing pipeline selection system using machine learning that analyzes medical image data and corresponding textual data to dynamically select the most appropriate pipeline based on extracted features such as modality, mode, view, and anatomical structures, incorporating text analytics, metadata parsing, and medical image analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed algorithms are used for medical image analysis, then the system is simple to implement, but it can only operate on specific types of medical imaging technology and requires user specification
Solution Approach 1:
The system automatically determines the appropriate processing pipeline by analyzing metadata and characteristics of the medical image itself, without requiring user specification. The algorithm self-configures based on the input image properties, eliminating the need for manual intervention while maintaining adaptability across different imaging modalities.
Solution Approach 2:
The system dynamically adjusts processing parameters and pipeline selection based on the detected characteristics of the medical image (modality, anatomical region, imaging parameters). By changing parameters according to the specific input, the system achieves versatility across different imaging types without requiring separate fixed algorithms for each modality.
2Reliability
If user specification is required for algorithm selection, then the system maintains control and transparency, but it increases the burden on users and potential for incorrect application
Solution Approach 1:
The system performs automatic pipeline determination by analyzing the medical image metadata and characteristics, eliminating the need for user specification. This self-configuring approach reduces user burden to zero while improving reliability by using objective image-based criteria rather than subjective user selection.
Solution Approach 2:
The system uses the medical image metadata and characteristics as feedback to automatically determine the appropriate processing pipeline. By continuously analyzing the input image properties and adjusting pipeline selection accordingly, the system achieves high accuracy without requiring user intervention or expertise.
3Extent of automation
If fixed algorithms are used, then the system is easy to operate, but it lacks automation in determining appropriate analytics for diverse medical image characteristics
Solution Approach 1:
The system automatically determines the processing pipeline by analyzing the medical image itself, achieving full automation. The algorithm extracts relevant characteristics from the image metadata and characteristics, then self-selects the appropriate pipeline without human intervention, maximizing automation while managing complexity through systematic analysis.
Solution Approach 2:
The system performs preliminary analysis of the medical image metadata and characteristics before selecting the processing pipeline. By pre-analyzing the input image properties and preparing the appropriate pipeline configuration in advance, the system achieves automation while organizing complexity into structured, manageable steps.
Data Source
AI summary
Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.