Semantic Network for Automated Cranial Image Diagnosis
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Solution Overview
Problem
Current diagnostic frameworks for brain disorders, such as multiple sclerosis, face challenges in efficiently integrating and processing large amounts of data from various sources, requiring specialized expertise and time, and lack effective methods for rapid and accurate neurological diagnosis.
Innovation Solution
An apparatus and method utilizing a semantic network to process cranial image data, incorporating it into a knowledge model, and comparing it to pathological condition prediction elements to generate indications of neurological conditions, enabling efficient and automated diagnosis without the need for skilled image processing or computer programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data integration methods are used to integrate cranial image data with research databases, then diagnostic accuracy can be improved, but the time required for processing and the complexity of the system increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing and standardizing cranial image data before integration into research databases. The system performs automated image registration, normalization, and feature extraction in advance, so that when diagnostic queries are made, the data is already prepared and structured for rapid comparison with reference databases, significantly reducing processing time while maintaining diagnostic accuracy
Solution Approach 2:
The patent introduces an intermediary layer consisting of automated image processing algorithms and data transformation protocols that mediate between raw cranial image data and research databases. This intermediary system handles the complex integration tasks automatically, translating diverse image formats and resolutions into standardized representations that can be efficiently queried and compared without requiring manual intervention
2Measurement precision
If manual image processing and data integration are performed by experts, then diagnostic accuracy is maintained, but the complexity of operation and need for specialized expertise increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform image processing, data integration, and diagnostic comparison tasks without requiring specialized human expertise. The automated algorithms handle image registration, feature extraction, and database querying independently, allowing medical professionals to obtain diagnostic results by simply inputting patient data without needing to understand the complex processing steps involved
Solution Approach 2:
The patent replaces manual mechanical image processing operations with automated computational algorithms. Instead of experts manually analyzing and integrating cranial images with research databases, the system uses computer-based image processing software and automated data integration protocols to perform these tasks, substituting human expert labor with mechanical-computational systems that maintain accuracy while reducing operational complexity
3Reliability
If comprehensive data from multiple sources is integrated, then diagnostic reliability improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive data integration process into distinct modular components: image acquisition module, preprocessing module, feature extraction module, database integration module, and diagnostic comparison module. Each module handles a specific aspect of data processing independently, allowing the system to manage complex multi-source data integration through organized, manageable segments rather than a monolithic complex system
Solution Approach 2:
The patent implements universality by designing a multi-functional integrated system that can handle multiple types of cranial images (MRI, CT, PET), integrate with various research databases, and perform different diagnostic comparisons using the same core architecture. This universal platform approach allows comprehensive data integration across diverse sources while avoiding the need for separate specialized systems for each data type or database
Data Source
AI summary
A computer-implemented method for computing a pathological condition of a subject, comprising obtaining (10) initial cranial image data of a subject from an input interface, and incorporating the initial cranial image data into a knowledge model comprised within a semantic network stored in a memory performing (12), via a processor, at least one processing sequence on the initial cranial image data using the semantic network to thus provide, in the semantic network, at least one element comprising topographical data of the subject's brain, or a portion of the subject's brain, referenced to a reference coordinate system wherein the at least one processing sequence performs at least one state iteration of at least a portion of the semantic network from a first state into a second state comparing (14) the topographical data of the subject's brain to one, or more pathological condition prediction elements of the semantic network to form an indication of a pathological condition of the subject, and generating (16) an additional element in the semantic network comprising the indication of the pathological condition of the subject.