AI Heart Condition Identification Using Predictive Models
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Solution Overview
Problem
Manual review of diagnostic test results and imaging for non-human subjects is time-consuming and prone to human errors and biases, leading to resource strain and delayed diagnoses.
Innovation Solution
A computer-implemented method using machine-learning logic to analyze medical images and medical information, determining dimensional features of the heart and predicting heart disease likelihood, trained on labeled data to provide automated and accurate diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used by lab technicians, then diagnostic accuracy can be maintained through human expertise, but time consumption and resource strain increase significantly
Solution Approach 1:
The patent introduces an intermediary AI system that acts as a bridge between manual review methods and automated processing. The AI pre-processes and analyzes medical images, presenting curated findings to technicians for verification. This intermediary layer maintains diagnostic accuracy through human expertise while dramatically reducing time consumption by filtering out obvious cases and highlighting only those requiring human judgment.
Solution Approach 2:
The diagnostic process is segmented into two distinct phases: automated AI analysis for initial assessment and human technician review for confirmation of critical findings. This segmentation allows routine cases to be processed quickly by AI while reserving human expertise for complex or ambiguous cases, thereby reducing overall time consumption without sacrificing diagnostic accuracy.
2Productivity
If more lab technicians are deployed to reduce review time, then diagnostic speed improves, but resource costs and operational complexity increase
Solution Approach 1:
The AI system performs self-service by automatically analyzing medical images, generating preliminary diagnoses, and prioritizing cases for human review. This self-capability eliminates the need to deploy additional technicians to increase diagnostic speed, as the AI handles the bulk of analysis work independently, thereby improving productivity without increasing operational complexity.
Solution Approach 2:
The patent replaces the mechanical system of human technicians manually reviewing all cases with an automated AI analysis system. This substitution dramatically improves diagnostic speed by processing images much faster than human reviewers while reducing operational complexity by eliminating the need to coordinate multiple technicians' schedules and workloads.
3Loss of time
If automated AI analysis is fully implemented, then time consumption and resource strain are reduced, but potential loss of nuanced human judgment and interpretive flexibility occurs
Solution Approach 1:
The AI system performs preliminary analysis of medical images before human technicians review them. This preliminary action prepares curated case summaries and highlights key findings, allowing technicians to focus their interpretive expertise on critical aspects rather than starting from scratch. This approach reduces time consumption while preserving interpretive reliability by maintaining human judgment for final interpretation.
Solution Approach 2:
The system incorporates feedback loops where AI analyses are reviewed and validated by technicians, and their corrections feed back into the AI training process. This feedback mechanism ensures that nuanced human judgment is preserved while continuously improving AI accuracy, thereby reducing time consumption without compromising interpretive reliability.
Data Source
AI summary
An example computer-implemented method for identifying a heart condition in a non-human subject includes receiving a medical image of the non-human subject, determining by a processor executing a first machine-learning logic and based on the medical image a dimensional feature of a heart of the non-human subject, displaying the dimensional feature of the heart on a graphical user interface, receiving medical information associated with the non-human subject on the graphical user interface, determining by the processor executing a second machine-learning logic and based on the medical information and the dimensional feature of the heart a likelihood of a heart disease, and displaying the likelihood of the heart disease on the graphical user interface.