Deep Learning Neuromodulation Therapy Optimization
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Solution Overview
Problem
Current systems for optimizing neuromodulation therapy for patients with neurological disorders, such as epilepsy, rely heavily on clinician experience and trial-and-error methods, which are time-consuming and inefficient, as they do not effectively leverage data from large patient populations to determine optimal therapy parameters.
Innovation Solution
A deep learning-based system that processes EEG records from both individual patients and a large patient population to identify similar patterns and associated clinical information, allowing for the automatic extraction of relevant therapy parameter values that can improve treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional computer systems are used to store and present patient data, then data storage and access are achieved, but the data is presented in isolation without leveraging data from other patients, limiting clinical decision-making effectiveness
Solution Approach 1:
The patent combines individual patient data with population-level data from multiple patients to create a comprehensive analytical system. The deep learning system integrates EEG records, therapy parameters, and outcomes across many patients to identify patterns that inform individualized treatment decisions, thereby utilizing information that would be lost if only individual patient data were considered.
Solution Approach 2:
The system serves multiple functions: it stores individual patient data, analyzes population-wide patterns, identifies similar patients, and provides personalized treatment recommendations. This multi-functional approach allows a single system to address both data storage needs and advanced clinical decision-making requirements.
2Manufacturing precision
If clinicians manually adjust multiple therapy parameters through trial and error, then personalized treatment optimization is pursued, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis by pre-processing and analyzing data from large patient populations to identify effective therapy parameter combinations before they are needed for individual patient care. When a new patient requires optimization, the system has already prepared pattern recognition models and similarity metrics that can be quickly applied, eliminating the need for clinicians to start trial-and-error processes from scratch.
Solution Approach 2:
The system identifies patients with similar characteristics and outcomes, then copies or adapts the successful therapy parameter configurations from those similar patients to the current patient. This allows rapid parameter optimization by leveraging proven treatments from comparable cases rather than relying solely on manual trial and error.
3Extent of automation
If deep learning algorithms are applied to EEG records from large patient populations, then automated feature extraction and pattern recognition are achieved, but the system requires significantly more training data compared to traditional machine learning
Solution Approach 1:
The system performs preliminary data collection and aggregation from large patient populations, building extensive training datasets in advance. This pre-prepared data infrastructure enables the deep learning algorithms to be trained comprehensively, allowing automated feature extraction to function effectively when clinically needed.
4Reliability
If clinicians rely on experience and published research to inform decisions, then some level of expertise-based optimization is achieved, but the process remains iterative and dependent on office visit frequency
Solution Approach 1:
The system implements continuous feedback by analyzing patient responses to therapy in real-time, comparing outcomes with patterns from similar patients, and automatically recommending parameter adjustments. This creates a closed-loop system where treatment decisions are continuously refined based on actual patient responses, eliminating the need to wait for periodic office visits to assess treatment effectiveness.
Data Source
AI summary
Information relevant to making clinical decisions for a patient is identified based on electrical activity records of the patient's brain and electrical activity records of other patients' brains. A deep learning algorithm is applied to an electrical activity record of the patient, i.e., an input record, and to a set of electrical activity records of other patients, i.e., a set of search records, to obtain an input feature vector of the patient and a set of search feature vectors, each including features extracted by the deep learning algorithm. A similarities algorithm is applied to the input feature vector and the set of search feature vectors to identify a subset of search records most like the input record. Clinical information associated with one or more search records in the identified subset of search records is extracted from a database and used to make decisions regarding the patient's neuromodulation therapies.