AI Candidate Scoring for Spinal Neuromodulation Response
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Solution Overview
Problem
Existing treatments for chronic back pain are costly, addictive, temporary, ineffective, or require long recovery times, and do not provide adequate relief for the majority of patients, with a small percentage being surgically eligible, and there is a lack of reliable methods to identify patients likely to respond favorably to spinal neuromodulation procedures.
Innovation Solution
A computer-implemented method using a combination of indicators, including imaging and biomarker analysis, to generate an objective score predicting the likelihood of a favorable response to spinal neuromodulation procedures like basivertebral nerve ablation, utilizing artificial intelligence techniques and trained algorithms to analyze MRI, CT, and other imaging modalities for vertebral endplate characteristics and biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing treatments for chronic back pain are used, then patients receive conventional therapy, but treatment effectiveness is low and recovery time is long
Solution Approach 1:
The system performs preliminary identification of suitable candidates using AI analysis of imaging and biomarker data before treatment. This preliminary screening ensures that only patients with high probability of favorable response undergo spinal neuromodulation, thereby improving overall treatment effectiveness while reducing unnecessary recovery time for unsuitable candidates
Solution Approach 2:
The system uses automated AI algorithms to analyze imaging data and biomarkers, generating objective scores and treatment recommendations without requiring extensive manual clinical assessment. This self-service approach accelerates the evaluation process, reducing the time from diagnosis to treatment decision while maintaining high reliability through algorithmic consistency
2Measurement precision
If spinal neuromodulation procedures are performed without candidate identification, then treatment may be provided, but false positives and negatives increase
Solution Approach 1:
The assessment system is segmented into distinct analytical components: imaging data analysis, biomarker analysis, AI-based score generation, and treatment recommendation. This segmentation allows each component to be optimized independently, improving measurement precision through specialized analysis while managing overall system complexity through modular architecture
Solution Approach 2:
The AI-based assessment system serves multiple functions: it analyzes diverse imaging modalities (MRI, CT, X-ray), processes various biomarker types, generates predictive scores, and provides treatment recommendations. This multi-functionality consolidates what would otherwise require multiple separate assessment tools into a single unified system, improving precision without proportionally increasing complexity
3Reliability
If comprehensive imaging and biomarker analysis is performed, then treatment accuracy improves, but analysis complexity increases
Solution Approach 1:
The AI algorithm acts as an intermediary that processes complex imaging and biomarker data, transforming multifaceted input information into a simplified objective score and clear treatment recommendation. This intermediary layer handles the complexity of comprehensive analysis internally while presenting simplified, actionable output to clinicians, thereby improving prediction accuracy without exposing the full complexity of the analysis system
Data Source
AI summary
Described herein are various implementations of systems and methods for determining likelihood of a patient favorably responding to a neuromodulation procedure based on a quantitative or objective score or determination based on a plurality of indicators of pain (e.g., chronic low back pain stemming from one or more vertebral bodies or vertebral endplates of a patient). The systems and methods may involve application of artificial intelligence techniques (e.g., trained algorithms, machine learning or deep learning algorithms, and/or trained neural networks).