Entropy-Based Neurological Treatment Selection
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Solution Overview
Problem
Selecting the most effective treatment for neurological conditions, such as major depressive disorder, is challenging due to individual neurological differences, as existing methods lack a systematic approach to identify personalized treatment options based on brain activity patterns.
Innovation Solution
The method involves obtaining fMRI scans to identify regions of interest, calculating entropy metrics, generating similarity indices, and using a machine learning model to select appropriate treatments like accelerated theta burst stimulation or pharmacological treatment based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional treatment selection methods are used, then treatment options can be provided to patients, but the effectiveness is low due to lack of personalization based on individual neurological differences
Solution Approach 1:
The patent applies local quality by calculating entropy metrics for specific regions of interest (ROIs) in the brain rather than treating the entire brain uniformly. Different ROIs are identified and analyzed individually to capture local neurological characteristics, enabling personalized treatment selection based on regional brain activity patterns.
Solution Approach 2:
The patent changes parameters by using entropy metrics as a new parameter to characterize brain activity patterns. Instead of relying on traditional clinical assessment parameters, the system calculates entropy values from fMRI data to quantify the complexity and unpredictability of neural activity, providing an objective basis for treatment selection.
2Reliability
If entropy metrics and machine learning models are used to personalize treatment, then treatment effectiveness improves, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that bridges the gap between complex entropy metric calculations and simple treatment recommendations. The model processes the high-dimensional entropy data and translates it into actionable treatment decisions, reducing the complexity burden on clinicians while maintaining personalization benefits.
Solution Approach 2:
The patent replaces the mechanical system of manual clinical assessment with an automated computational system. Instead of relying on subjective clinician judgment, the system uses algorithmic processing of fMRI data and entropy calculations to objectively determine treatment options, reducing human cognitive load and increasing consistency.
3Measurement precision
If fMRI scans and entropy calculations are performed, then personalized treatment identification improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-processing fMRI data to extract entropy metrics before treatment decision-making. The system performs entropy calculations on identified regions of interest in advance, creating a ready-to-use feature set that can be quickly evaluated by the machine learning model, reducing the time required at the point of treatment selection.
Solution Approach 2:
The patent extracts only the essential entropy metrics from the full fMRI dataset, focusing on specific regions of interest rather than analyzing the entire brain volume. This extraction approach reduces the data processing burden while retaining the most relevant information for treatment prediction, balancing precision with efficiency.
Data Source
AI summary
Systems and methods for entropy-based treatment in accordance with embodiments of the invention are illustrated. One embodiment includes a method for treating a neurological condition, including obtaining a functional magnetic resonance imaging (fMRI) scan of a patient's brain, identifying a plurality of regions of interest (ROIs) in the fMRI scan, calculating entropy metrics for each ROI in the plurality of ROIs, selecting a treatment from a plurality of treatments for the neurological condition based on the entropy metrics, and provide the selected treatment to the patient. In a further embodiment, calculating entropy metrics includes generating multiscale entropy curves for each ROI, calculating the area under each entropy curve, assigning each ROI as positive or negative sign based on the entropy curve of each ROI, generating a similarity index for each ROI, and signing the similarity index for each ROI using the assigned sign of each ROI.