US 2026/0311331 A1Published application

Neural network system for spinal cord stimulation therapy optimization

AssigneeWavegate Corporation

InventorsErich W. Wolf, II, George R. Schultz

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Abstract

A system for optimizing spinal cord stimulation therapy uses neural network models to predict sensation patterns from electrode geometry and stimulation parameters. The system captures patient-drawn sensation maps on a standardized physical doll or on a digital avatar rendered on a tablet computer, digitizes the markings, and associates them with X-ray-derived electrode positions and applied stimulation settings. Training data collected across multiple patients enables the neural network to learn stimulus-response relationships that generalize across patients. Given a target pain area, the trained network recommends stimulation settings predicted to produce therapeutic coverage. Technical improvements include predictive rather than reactive programming, cross-patient learning, reduced programming time from hours to seconds, and extended implanted battery life through reduced wireless transmissions. The trained neural network serves as a compressed repository encoding paresthesia data from training trials, such that effective stimulation patterns are retrievable for new patients.

Claims32

Dependent claims

A collective paresthesia data storage system comprising:

a server operatively connected to a patient image capture device, an X-ray image capture device, and to a spinal cord stimulator;
the server having a first memory;
the spinal cord stimulator having a second memory;
the patient image capture device having a third memory;
the X-ray image capture device having a fourth memory;
a trained neural network stored in the first memory, wherein the trained neural network encodes paresthesia data collected from a plurality of stimulation trials across a plurality of patients, and wherein the trained neural network has been trained to learn a relationship between a set of electrode contact positions, a set of stimulation settings, and a set of paresthesia patterns from the plurality of stimulation trials;
and
a set of instructions stored in the first memory, the second memory, the third memory, and the fourth memory that when executed, cause the system to:
receive, via the X-ray image capture device, an X-ray image showing implanted electrode contacts of the spinal cord stimulator;
receive, via the patient image capture device, a doll image showing a target pain area marked by a new patient;
derive the set of electrode contact positions from the X-ray image;
derive a set of proposed stimulation settings;
retrieve, using the trained neural network, a predicted paresthesia pattern corresponding to the set of proposed stimulation settings and the set of electrode contact positions;
compare the predicted paresthesia pattern to the target pain area;
iteratively adjust, using the trained neural network, the set of proposed stimulation settings until the predicted paresthesia pattern corresponds to the target pain area, thereby producing a recommended set of stimulation settings;
and
output the recommended set of stimulation settings to the spinal cord stimulator;
wherein the trained neural network serves as a compressed repository of the paresthesia data collected from the plurality of stimulation trials, such that effective stimulation patterns discovered across the plurality of patients are retrievable for the new patient.

15 more elements

A predictive stimulation programming system comprising:

a server operatively connected to a camera and to a spinal cord stimulator;
the server having a first memory containing a trained neural network;
the spinal cord stimulator having a second memory;
the camera having a third memory;
and
a set of instructions stored in the first memory, the second memory, and the third memory, that when executed, cause the system to:
receive, via the camera, an X-ray image showing implanted electrode contacts of the spinal cord stimulator;
receive, via the camera, a doll image showing a target pain area marked by a patient;
derive electrode contact positions from the X-ray image;
standardize the electrode contact positions and the doll image to a common coordinate system and a fixed image size;
derive a set of proposed stimulation settings;
execute the trained neural network to produce a predicted sensation image from the set of proposed stimulation settings and the electrode contact positions;
compute a difference score between the predicted sensation image and the doll image;
iteratively adjust the set of proposed stimulation settings to reduce the difference score;
and
output a recommended set of stimulation settings to the spinal cord stimulator;
wherein the recommended set of stimulation settings is predicted to produce a paresthesia pattern corresponding to the target pain area prior to delivery of stimulation.

14 more elements

A predictive stimulation programming system comprising:

a server operatively connected to a set of image intake devices and a set of spinal cord stimulators;
a set of X-ray machines;
the server having a first set of memories;
the set of image intake devices having a second set of memories;
the set of spinal cord stimulators having a third set of memories;
and
a set of instructions stored in the first set of memories, the second set of memories, and the third set of memories, that when executed, cause the system to perform:
a data collection phase comprising:
displaying, on the set of X-ray machines, a set of X-ray images showing implanted electrode contacts of the set of spinal cord stimulators;
receiving, from the set of image intake devices, a set of photographs of the set of X-ray images;
deriving a set of electrode contact positions from the set of X-ray images;
applying, to the set of spinal cord stimulators, a set of stimulation settings during a set of programming sessions with a set of patients;
receiving, from the set of image intake devices, a set of doll images showing paresthesia markings made by the set of patients in response to the set of stimulation settings;
and
standardizing the set of electrode contact positions, the set of stimulation settings, and the set of doll images into a set of training examples;
a training phase comprising:
training a neural network on the set of training examples to learn a relationship between the set of electrode contact positions, the set of stimulation settings, and a set of paresthesia patterns;
and
an inference phase comprising:
receiving a target pain area marked by a new patient;
executing a trained neural network to generate a recommended set of stimulation settings predicted to produce paresthesia covering the target pain area;
and
outputting the recommended set of stimulation settings to one of the set of spinal cord stimulators;
wherein the relationship between the set of electrode contact positions, the set of stimulation settings, and the set of paresthesia patterns learned from the set of patients changes the recommended set of stimulation settings.

21 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/638,765Filed 3 Apr 2026
Priority: 4 Apr 2025
Published: 8 Oct 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G06T 7/0012
Inspection of images, e.g. flaw detection: biomedical image inspection
G06N 3/048
Architecture, e.g. interconnection topology: activation functions
G06T 2207/10116
Image acquisition modality: x-ray image
G06T 2207/20081
Special algorithmic details: training; Learning