AI Biomarker Bank for Liver Lesion Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current manual detection and characterization of liver lesions in medical imaging are time-consuming, labor-intensive, and subjective, and conventional machine learning approaches struggle to represent user-defined requirements effectively, especially in analyzing liver lesions across different patients.
Innovation Solution
An AI-driven biomarker bank is created to store lesion-related features extracted from medical images, allowing for standardized analysis of patient populations, including measurement, modality-specific, patient-specific, and longitudinal features, enabling automated categorization and treatment planning based on LI-RADS guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection and characterization of liver lesions is performed by radiologists, then subjective expert judgment can be applied, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent segments the liver lesion analysis into multiple independent feature extraction components including measurement features, modality-specific features, patient-specific features, contrast phase differential features, and longitudinal differential features. This segmentation allows parallel processing of different feature types, maintaining comprehensive analysis while reducing overall processing time through automated computation of each feature category.
Solution Approach 2:
The patent performs preliminary extraction and storage of lesion-related features in a biomarker bank before actual analysis is needed. By pre-computing and organizing features such as measurement features, texture features, and longitudinal differential features from medical images, the system prepares data in advance, enabling faster query and analysis when clinical decisions are required.
2Productivity
If conventional machine learning approaches are used for liver lesion analysis, then automated processing is achieved, but user-defined requirements and complex lesion characterizations cannot be effectively represented
Solution Approach 1:
The patent implements a dynamic feature extraction framework that adapts to different user-defined requirements and analysis scenarios. The system can dynamically select and combine different feature types (measurement, modality-specific, patient-specific, contrast phase differential, longitudinal differential) based on specific clinical questions, allowing flexible adaptation to various analysis needs while maintaining automated processing efficiency.
Solution Approach 2:
The patent changes the parameter representation by extracting and storing multiple types of lesion-related features in standardized formats, including measurement features, modality-specific features, patient-specific features, contrast phase differential features, and longitudinal differential features. This multi-parameter approach enables the system to represent complex user-defined requirements that cannot be captured by conventional single-parameter machine learning approaches.
3Measurement precision
If comprehensive lesion features are extracted and stored for all patients, then accurate population-level analysis is enabled, but data storage and processing complexity increases
Solution Approach 1:
The patent creates a universal biomarker bank that stores lesion-related features in standardized formats applicable to all patients and multiple analysis scenarios. The same feature extraction infrastructure supports various analysis types including population distribution analysis, patient similarity identification, treatment plan determination, and automated LI-RADS categorization, reducing system complexity through a single multi-functional platform.
Solution Approach 2:
The patent extracts and stores standardized feature representations (copies) of liver lesions from medical images in a biomarker bank. These feature copies include measurement features, modality-specific features, patient-specific features, contrast phase differential features, and longitudinal differential features, which can be efficiently stored and reused for multiple analysis purposes without requiring access to the original complex medical images.
Data Source
AI summary
Systems and methods for performing an analysis on a patient population are provided. A biomarker bank storing lesion-related features extracted from medical images of a patient population is maintained. An analysis is performed on the patient population based on the lesion-related features stored in the biomarker bank. Results of the analysis.