Patient Deviation Score Analysis for Neurodegenerative Disease Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting and monitoring neurodegenerative disorders like Alzheimer's and Parkinson's are challenging due to difficulty in early detection and standardized quantification, leading to inconsistent and non-standardized diagnoses, and the separation of analysis and reporting in distinct informational silos.
Innovation Solution
A system and method that access and process patient image and non-image deviation scores relative to standardized data, generating visual outputs to facilitate diagnosis, integrating multiple medical diagnosis test results across different time points and modalities, and calculating scores like Z-scores, T-scores, D-scores, and DT-scores to create comprehensive reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If statistical deviation methods are used to compare patient data to normal databases, then detection capability is improved, but diagnostic consistency and standardization deteriorate due to subjectivity
Solution Approach 1:
The patent replaces the manual, subjective visual assessment mechanism with an automated computer-based analysis system. The system automatically processes patient data, compares it to normal databases using statistical methods, and generates standardized deviation reports, eliminating human subjectivity while maintaining detection capability.
Solution Approach 2:
The patent transforms qualitative diagnostic assessments into quantitative measurements by calculating statistical deviation scores (e.g., Z-scores, T-scores). This parameter transformation allows for standardized, objective comparison of patient data across different experts and institutions, improving diagnostic consistency.
2Adaptability or versatility
If imaging and laboratory analyses are performed in separate informational silos, then specialization is improved, but comprehensive diagnosis deteriorates due to fragmented information
Solution Approach 1:
The patent merges previously separate imaging and laboratory analysis workflows into a single integrated system. The system accepts multiple types of patient data (imaging, laboratory results, clinical information), processes them together using unified statistical methods, and generates comprehensive deviation reports that synthesize all information sources.
Solution Approach 2:
The patent creates a universal analysis platform that can handle multiple types of medical data (imaging, laboratory results, clinical measurements) through a single standardized process. This multi-functional system maintains the advantages of specialized analysis while enabling comprehensive integration of all patient information.
3Reliability
If multiple types of patient data are collected, then diagnostic comprehensiveness is improved, but data integration complexity increases
Solution Approach 1:
The patent standardizes diverse patient data types by transforming them into a common parameter format (statistical deviation scores). All input data, regardless of type or source, are converted to standardized Z-scores or T-scores that can be directly compared and integrated, simplifying the integration process while maintaining comprehensive diagnostic information.
Data Source
AI summary
A system comprising a memory device having a plurality of routines stored therein, a processor configured to execute the plurality of routines stored in the memory device, the plurality of routines comprising: a routine configured to effect, when executed, accessing of patient deviation scores indicative of differences between patient data and reference data representative of a population segment, wherein the patient deviation scores are derived from longitudinal data of the patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data; a routine configured to effect, when executed, identifying a trend in the patient deviation scores for at least one clinical parameter; a routine configured to effect, when executed, generating of a report including a visual indication of the trend; and a routine configured to effect, when executed, outputting of the report. The report includes one or more views including Z, T, D, DT, and D feedback on T views, using image and non-image data.