In-Silico Cardiac Database for Explainable Disease Prediction
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Solution Overview
Problem
Existing cardiac disease diagnosis methods, particularly those using AI, face challenges in providing accurate and explainable predictions due to the 'black-box problem, lacking a clear understanding of the underlying physiological mechanisms, and require improved methods for early detection and development of therapeutic strategies.
Innovation Solution
An in-silico cardiac disease database is utilized, comprising a large number of cardiac models with varied macro and micro factors, where electrocardiogram and echocardiographic parameters are analyzed to identify influencing factors and predict cardiac diseases with high accuracy, allowing for precise diagnosis and therapeutic method development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-based automatic diagnostic tools are used for cardiac disease diagnosis, then diagnostic efficiency is improved, but diagnostic explainability and physiological mechanism understanding deteriorate due to the black-box problem
Solution Approach 1:
The patent introduces an in-silico cardiac disease database as an intermediary between actual clinical data and AI diagnostic tools. This database contains simulated electrocardiogram and echocardiographic parameters from cardiac models with known physiological variations, serving as a bridge that provides both diagnostic capability and physiological interpretability without requiring direct access to the black-box AI internal mechanisms
Solution Approach 2:
The patent performs preliminary generation of the in-silico cardiac disease database before actual diagnosis, pre-computing simulated parameters across various cardiac conditions and storing them with their corresponding physiological factor variations. This preliminary action enables subsequent rapid diagnosis while maintaining physiological interpretability through the pre-established database
2Measurement precision
If in-silico cardiac disease database is created with large number of cardiac models, then measurement precision of cardiac disease prediction is improved, but device complexity and computational resources required worsen
Solution Approach 1:
The patent creates virtual copies of cardiac models in the in-silico database that replicate real cardiac physiology and pathology without requiring physical experiments or complex real-time computational systems. These copied models can be pre-computed and stored, reducing the complexity of the actual diagnostic system while maintaining high prediction accuracy
Solution Approach 2:
The patent performs comprehensive cardiac simulations in advance to populate the in-silico database with pre-computed results across various cardiac conditions. This preliminary computation transfers the computational burden from the diagnostic phase to the database creation phase, simplifying the actual diagnostic system while maintaining high measurement precision
3Manufacturing precision
If in-silico cardiac disease database is utilized for precision medicine, then therapeutic strategy development is improved, but time and resources required for individual patient analysis worsen
Solution Approach 1:
The patent creates a universal in-silico cardiac disease database that serves multiple functions: it can be used for individual patient diagnosis, therapeutic strategy development, and population-level analysis. This multi-functional database eliminates the need for separate analysis systems and reduces time investment for each application while maintaining high precision across all uses
Data Source
AI summary
An information processing technology capable of increasing prediction accuracy of causing cardiac diseases, performing appropriate therapy and increasing efficiency of drug development is constructed by utilizing “in-silico cardiac disease database.” An electrocardiogram and the like obtained from “in-silico cardiac disease database” which is the database storing a simulation result of a cardiac model of virtual disease where various factor related to cardiac disease are changed are inputted to a classifier for performing an automatic diagnosis of the cardiac disease, and an influencing factor associated with the cardiac disease is identified as a biomarker by comparing distributions of a variation amount of a factor between a positive group and a negative group. In addition, precise diagnosis of an individual is easily performed by identifying the cardiac model having the electrocardiogram and the like appropriate to the actual electrocardiogram and the like of the individual in the in-silico cardiac disease database. Furthermore, the process of drug development is efficiently performed by extracting a group having a predetermined feature from the in-silico cardiac disease database and simply evaluating an effect of an administration of a medical agent.