Diagnostic Model Generation for Heart Conditions
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Solution Overview
Problem
Current diagnostic methods for heart conditions, such as echocardiography, rely on subjective classification and are time-consuming, failing to account for changing disease pathophysiology, geographical variations, and evolving patient populations.
Innovation Solution
A system and method for generating a diagnostic model using a processor to analyze image data sets, identify features, calculate metrics, and compile a model based on outcome data, allowing for refinement and adaptation over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If subjective classification by expert cardiologists is used to determine myocardial wall motion, then diagnostic interpretation can be performed, but the process becomes time-consuming and less efficient
Solution Approach 1:
The patent replaces the mechanical system of manual subjective classification by cardiologists with an automated image processing system that uses computational algorithms to objectively analyze myocardial wall motion. The processor automatically segments the left ventricle into segments, tracks myocardial motion, and generates wall motion scores, eliminating the time-consuming manual interpretation process while maintaining diagnostic accuracy.
2Adaptability or versatility
If fixed diagnostic models are used, then consistency in diagnosis is maintained, but the system cannot adapt to changing disease pathophysiology, geographical variations, and evolving patient populations
Solution Approach 1:
The patent implements a dynamic diagnostic model that can adapt to changing conditions. The system incorporates feedback mechanisms where outcome data is used to refine and update the diagnostic model over time. The processor can adjust segmentation parameters, motion tracking algorithms, and scoring thresholds based on accumulated data and changing disease patterns, allowing the model to evolve while maintaining diagnostic consistency through structured update protocols.
Solution Approach 2:
The system incorporates feedback loops where outcome data from patient diagnoses is fed back into the diagnostic model. The processor analyzes this feedback to refine segmentation algorithms, adjust motion analysis parameters, and update reference values for wall motion scoring. This continuous feedback mechanism enables the system to adapt to changing disease pathophysiology and population characteristics while maintaining diagnostic reliability.
Data Source
AI summary
The present disclosure relates to a system (100) for generating a diagnostic model. The system (100) includes a processor (108) configured to analyse a plurality of reference data sets. The reference data sets each include at least one image (230, 240). The analysis identities at least one feature in each image (230, 240). A metric is calculated in dependence on the at least one identified feature. Outcome data associated with at least some of the reference data sets is acquired. The diagnostic model is compiled in dependence on the at least one calculated metric and the associated outcome data. The present disclosure also relates to a method of generating a diagnostic model; and a non-transitory computer-readable medium.