Brain Wave Diagnosis Automation via Morphological Feature Recognition
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Solution Overview
Problem
Manual interpretation of medical images and EEG data for diagnosis is time-consuming and subjective, leading to variations in diagnosis and increased costs due to human involvement.
Innovation Solution
A system and method that digitally processes medical data using machine learning to recognize morphological features in images and brain wave scans, comparing them to learned data to determine probabilities and create visual diagnostic representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of medical images and EEG data is used for diagnosis, then medical professionals can provide expert judgment and clinical context, but the process becomes time-consuming and subjective leading to variations in diagnosis
Solution Approach 1:
The patent creates a digital copy of the medical professional's diagnostic reasoning process by training machine learning models on labeled medical images and EEG data. The system learns to replicate expert pattern recognition and diagnostic decision-making, producing consistent diagnostic results without requiring actual human interpretation of each case.
Solution Approach 2:
The patent replaces the mechanical system of manual human interpretation with an automated computational system. Machine learning algorithms process medical images and EEG data through defined computational operations, substituting the human cognitive process with a deterministic algorithmic approach that eliminates variability and time constraints.
2Reliability
If manual interpretation of medical images and EEG data is used for diagnosis, then clinical expertise and judgment can be applied, but human involvement increases costs
Solution Approach 1:
The patent replaces the complex human cognitive system with a computational model that processes medical data through standardized algorithms. The machine learning system achieves consistent diagnostic reliability by applying the same learned patterns uniformly across all cases, eliminating the variability inherent in human interpretation.
Solution Approach 2:
The patent transforms the diagnostic process from a subjective human judgment process into an objective computational process with measurable parameters. The system outputs standardized diagnostic results with confidence scores, changing the nature of the output from qualitative human assessment to quantitative computational measurement.
3Measurement precision
If constant EEG monitoring is performed to predict seizures throughout the day, then seizure forecasting accuracy improves, but the time overhead becomes unmanageable
Solution Approach 1:
The patent applies partial monitoring by focusing computational resources on analyzing only the most diagnostically relevant features of EEG data rather than continuously processing all signals. The machine learning model identifies and prioritizes key patterns associated with seizure activity, achieving effective seizure prediction without requiring exhaustive analysis of every data point.
Solution Approach 2:
The patent replaces continuous manual EEG monitoring with an automated system that processes EEG data through machine learning algorithms. The computational system efficiently identifies seizure patterns in real-time without the time overhead of constant human review, maintaining high prediction accuracy while dramatically improving diagnostic efficiency.
Data Source
AI summary
Embodiments relate to digital image processing for diagnosis of a subject. More specifically, the embodiments relate to automation of diagnoses through data interpretation. An image is acquired from the subject. Elements are recognized within the image based on morphological features. The image is compared to learned data. Based on the comparison, a probability of a potential diagnosis(es) is calculated. A diagnosis of the subject is determined based on the potential diagnosis(es) and the calculated probability. The diagnosis may be changed based on a new image acquired from the subject.