Automated Medical Image Analysis Using ML Feature Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Historically, generated images have been unsuitable for automated review via computer-implemented systems, such as for automated diagnoses of medical conditions reflected within those images.
Innovation Solution
A computing system and method configured for applying objective image content analysis processes to determine the presence or absence of objective image characteristics, by retrieving appropriate image analysis rules and models, identifying reference features within the image, and utilizing those features to establish a scale and/or point of reference for absolute or relative characteristics of features within the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image analysis systems are implemented, then productivity and efficiency are improved, but the systems are historically unsuitable for automated review due to lack of objective measurement capabilities
Solution Approach 1:
The image analysis system segments the image into multiple regions of interest using machine learning models, identifying specific anatomical structures, lesions, or features. This segmentation enables automated measurement by dividing the complex image into manageable, analyzable components that can be individually evaluated against objective criteria.
Solution Approach 2:
The system transforms subjective visual assessment into objective quantitative measurements by applying scaling models that convert image features into measurable parameters. This parameter transformation enables automated analysis by expressing image characteristics in numerical form that can be compared against diagnostic thresholds and criteria.
2Measurement precision
If manual image review is used to ensure accurate diagnosis, then measurement precision is improved, but productivity and time consumption deteriorate
Solution Approach 1:
The system replaces manual mechanical review processes with automated computer-based analysis. Machine learning models and measurement algorithms substitute for human visual inspection, maintaining diagnostic accuracy through objective measurement while dramatically increasing processing speed and productivity.
Solution Approach 2:
The system incorporates feedback mechanisms where measurement results are continuously refined and validated. The automated analysis provides objective feedback on image characteristics, which can be used to adjust and improve diagnostic criteria, ensuring maintained or enhanced accuracy while enabling high-volume processing.
3Reliability
If comprehensive image analysis is performed to improve diagnostic accuracy, then reliability is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing images and pre-identifying regions of interest before comprehensive analysis. Machine learning models pre-segment images and highlight potential abnormalities, reducing the complexity of subsequent detailed measurement and analysis while maintaining reliable diagnostic results.
Solution Approach 2:
The system employs universal machine learning models and measurement algorithms that can analyze multiple types of medical images and diagnose various conditions using the same core technology platform. This multi-functionality reduces overall system complexity by avoiding the need for separate specialized systems for each diagnostic task.
Data Source
AI summary
Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.