Automated Biomedical Anomaly Detection via Multi-Field Data Segmentation
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Solution Overview
Problem
Current data analysis systems in the biomedical field are time-consuming and prone to human error, requiring individual expert analysis and significant processing time, which can delay the detection of abnormalities such as brain tumors.
Innovation Solution
A computing device-based method that automatically detects anomalies in datasets by inputting subject and reference datasets, preprocessing the data, and using similarity metrics to identify anomalies, with features like partitioning, segmentation, and deep learning for accurate detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual expert analysis is used to detect abnormalities in biomedical data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the biomedical data into multiple datasets and processes them through parallel computational pathways including signal processing, image processing, and machine learning algorithms. This segmentation enables simultaneous analysis of multiple data aspects, maintaining detection accuracy while reducing overall processing time through concurrent operations.
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated computational system that uses signal processing algorithms, image processing techniques, and machine learning models. This substitution eliminates the time-consuming nature of individual expert review while maintaining or improving detection precision through systematic multi-field analysis.
2Productivity
If automated machine learning systems are used for data analysis, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal multi-field data processing system that handles multiple types of biomedical data (signals, images, text) through integrated computational pipelines. The system uses standardized preprocessing, analysis, and interpretation modules that can process various data types, reducing overall system complexity while maintaining high productivity through versatile, reusable components.
Solution Approach 2:
The patent transforms complex biomedical data into standardized numerical parameters and features through signal processing and image processing algorithms. By converting diverse data types into uniform parameter representations, the system simplifies subsequent machine learning analysis while preserving critical diagnostic information, thereby reducing complexity without sacrificing analysis speed or accuracy.
3Measurement precision
If individual user input is required for each subject, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent implements self-service automated analysis pipelines that process biomedical data without requiring manual user input for each subject. The system automatically performs data preprocessing, feature extraction, anomaly detection, and result interpretation through integrated machine learning models, eliminating the need for repeated manual intervention while maintaining diagnostic accuracy through systematic computational analysis.
Solution Approach 2:
The patent performs preliminary automated processing of biomedical data including signal filtering, image enhancement, and feature extraction before formal analysis. By preparing and pre-processing data automatically in advance, the system reduces the need for manual user input during the actual diagnostic process, thereby maintaining measurement precision while significantly reducing overall diagnosis time.
Data Source
AI summary
Described herein relates to system and method for detecting an abnormal presence within at least one dataset. In an embodiment, the dataset may comprise 1D signals, and/or multidimensional signals (e.g., images). The multi-field data analysis system (hereinafter “system”) may be configured to detect at least one type of abnormality that may not be detected visually otherwise (e.g., analysis by an expert). In addition, the system may be configured to detect the size and/or shape of the abnormality. In this embodiment, the system may also retain the physical location (spatial information) of the abnormality. As such, the system may optimize computation and/or implementation due to the system's ability to project the data on any appropriate domain (e.g., transform domains). Moreover, the system may be configured to search for abnormalities and/or particular characteristics any biometric signal known in the art.