Diagnostic Image AI for Disease Feature Detection and Progression Prediction
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Solution Overview
Problem
Existing medical imaging systems often miss or underestimate clinically-relevant disease features, leading to inaccurate diagnoses and delayed detection of disease progression, which can worsen prognosis.
Innovation Solution
A system utilizing neural networks for feature extraction and disease progression prediction, incorporating a graphical user interface to analyze patient images, provide disease feature outputs, and predict future outcomes based on user inputs and patient-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinicians perform manual examination of diagnostic images, then the process is simple and quick, but clinically-relevant features are often missed or underestimated leading to diagnostic errors
Solution Approach 1:
The patent introduces an artificial intelligence system as an intermediary between the diagnostic image and the clinician. The AI system processes the image data and highlights clinically-relevant features, acting as a mediator that enhances human detection accuracy without replacing the clinician's role. This resolves the contradiction by providing automated feature detection (improving accuracy) while maintaining clinical oversight (managing complexity).
Solution Approach 2:
The patent replaces the manual mechanical examination process with an automated AI-based system. Instead of clinicians manually reviewing images, the system uses machine learning algorithms to automatically detect and prioritize clinically-relevant features. This substitution improves detection accuracy while the modular AI architecture manages system complexity through standardized processing pipelines.
2Reliability
If follow-up imaging sessions are conducted to detect unnoticed features, then diagnostic completeness improves, but the prognosis is much worse by the time features are detected
Solution Approach 1:
The patent applies preliminary action by having the AI system analyze and detect clinically-relevant features at the time of the initial image acquisition, rather than waiting for follow-up sessions. The system proactively identifies features that might be missed by clinicians, enabling early intervention before the disease progresses. This resolves the contradiction by improving diagnostic completeness at the earliest possible moment, eliminating the time delay associated with waiting for follow-up imaging.
3Reliability
If multiple imaging sessions are performed to ensure accurate diagnosis, then diagnostic reliability improves, but patient burden and healthcare costs increase
Solution Approach 1:
The patent implements feedback by having the AI system provide real-time analysis results to clinicians during the initial imaging session. The system processes the image data immediately and provides feedback on detected features, allowing clinicians to make informed decisions without requiring additional follow-up sessions. This resolves the contradiction by achieving high diagnostic reliability through immediate AI feedback, thereby improving healthcare efficiency by reducing the number of imaging sessions needed.
Data Source
AI summary
The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and/or descriptors of disease features that may be likely to develop over time.