Machine Learning Spinal Cord Stimulation Patient Selection
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Solution Overview
Problem
Current methods for predicting patient response to spinal cord stimulation (SCS) are inadequate, leading to suboptimal outcomes in 50% of patients and high financial burdens due to failed implants, as they lack objective criteria for patient selection and rely heavily on subjective physician experience.
Innovation Solution
A machine learning (ML) approach using unsupervised clustering and supervised classification to identify distinct patient clusters and develop individualized predictive models based on demographics, pain descriptors, psychiatric comorbidities, spinal imaging, and past SCS results, achieving 70-75% success in long-term response prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient selection for SCS is based on subjective physician experience, then the decision-making process is simple and quick, but the prediction accuracy of treatment outcome is low and unreliable
Solution Approach 1:
The patent introduces an intermediary system comprising a processor and trained machine learning models that act as a mediator between patient input data and treatment outcome prediction. This intermediary processes multiple patient features (demographics, pain descriptors, psychiatric comorbidities, spinal imaging, activity, medications, and past SCS results) through clustering and classification algorithms to generate objective predictions, thereby resolving the contradiction between simple decision-making and accurate prediction.
2Reliability
If traditional SCS implantation is performed without predictive modeling, then the procedure can be performed quickly, but the failure rate is high with 25-30% of implants failing to provide adequate long-term pain relief
Solution Approach 1:
The patent applies preliminary action by performing machine learning-based predictive modeling and patient clustering before SCS implantation. The system evaluates multiple patient features and generates treatment outcome predictions in advance, allowing clinicians to identify suitable candidates before the procedure. This preliminary assessment reduces post-implantation failures by 25-30% while the assessment time is minimized through automated processing of patient data.
3Measurement precision
If more patient features are evaluated to improve prediction accuracy, then the predictive performance increases to 70-75% success rate, but the data collection and processing complexity increases
Solution Approach 1:
The patent segments the prediction task into two distinct stages: first, unsupervised clustering groups patients into subpopulations based on similarities across multiple features; second, supervised classification models are applied within each cluster to predict treatment outcomes. This segmentation allows the system to evaluate comprehensive patient features (demographics, pain descriptors, psychiatric comorbidities, spinal imaging, activity, medications, and past SCS results) achieving 70-75% predictive performance while managing data processing complexity through modular architecture.
Data Source
AI summary
A system for predicting a spinal cord stimulation having a user interface for entry of features from a new patient and a machine learning engine having a cluster stage trained to evaluate the plurality of patient features to identify a cluster corresponding to the plurality of features of the patient from a plurality of clusters and a prediction stage trained to output a patient predicted outcome based a predictive model corresponding to the identified cluster. The plurality of features may comprise patient demographics, pain descriptors, pain questionnaire data, psychiatric comorbidities, spinal imaging, activity, medications, non-psychiatric comorbidities, and past spinal cord stimulation results.