# US 12,213,774 B1 · Apparatus and method for locating a position of an electrode on an organ model

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- Number: US 12,213,774 B1
- Document ID: US12213774B1
- Status: Granted
- Kind: B1
- Application number: 18/402,124
- Filed: 2024-01-02
- Granted: 2025-02-04
- Earliest claimed: 2024-01-02
- Assignees: nference, Inc.
- Applicants: nference, Inc.
- Inventors: Murali Aravamudan; Rakesh Barve; Suthirth Vaidya; Uddeshya Upadhyay; Abhijith Chunduru; Arjun Puranik; Sai Saketh Chennamsetty
- Web page: https://patentgrep.com/patent/US12213774B1

## CPC

- A61B 5/066: {Superposing sensor position on an image of the patient, e.g. obtained by ultrasound or x-ray imaging}
- A61B 8/0841: {for locating instruments}
- A61B 8/0883: {for diagnosis of the heart}
- A61B 8/12: in body cavities or body tracts, e.g. by using catheters
- A61B 8/4245: {involving determining the position of the probe, e.g. with respect to an external reference frame or to the patient}
- A61B 8/5261: {combining images from different diagnostic modalities, e.g. ultrasound and X-ray}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

An apparatus and method for locating a position of an electrode on an organ model. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an organ model configured to digitally represent an organ, receive a set of sensor data from at least a sensor including an ultrasound sensor, determine an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein determining the electrode position includes determining a model position within the organ model as a function of the set of sensor data and determining the electrode position within the model position of the organ model as a function of the set of sensor data and add a visual marker onto the electrode position in the model position of the organ model.

## Claims

### Claim 1

An apparatus for locating a position of an electrode on an organ model, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an organ model, wherein the organ model is configured to digitally represent an organ;
receive a set of sensor data from at least a sensor, wherein the at least a sensor comprises an ultrasound sensor;
determine an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein
determining the electrode position comprises:
determining a model position within the organ model as a function of the set of sensor data, wherein determining the model position further comprises:
generating first position training data, wherein the first position training data comprises correlations between exemplary sensor data and exemplary model positions;
training a first position machine-learning model of the position machine-learning module using the first position training data; and
determining the model position within the organ model using the trained first position machine-learning model; and
determining the electrode position within the model position of the organ model as a function of the set of sensor data, wherein determining the electrode position further comprises:
generating second position training data, wherein the second position training data comprises correlations between exemplary sensor data and exemplary electrode positions;
training a second position machine-learning model of the position machine-learning module using the second position training data, wherein the second position training data is iteratively updated on a feedback loop as a function of an output of the first position machine-learning model of the position machine-learning module; and
determining the electrode position within the organ model using the trained second position machine-learning model; and
add a visual marker onto the electrode position in the model position of the organ model.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to construct the organ model as a function of electrocardiogram data using a model machine-learning module.

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein the memory contains instructions further configuring the at least a processor to determine a cardiac model machine-learning model from a plurality of model machine-learning models of the model machine-learning module as a function of the electrocardiogram data.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the ultrasound sensor comprises an intracardiac echocardiography (ICE) catheter configured to sweep within the organ to generate the set of sensor data as a part of refinement of the organ model.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to generate a refined organ model as a function of the set of sensor data.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the ultrasound sensor comprises an electrode.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein the first position machine-learning model and the second position machine-learning model comprise a deep neural network.

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to transmit the visual marker added organ model to a remote device to display the visual marker added organ model to a user.

### Claim 9

A method for locating a position of an electrode on an organ model, the method comprising:
receiving, using at least a processor, an organ model, wherein the organ model is configured to digitally represent an organ;
receiving, using the at least a processor, a set of sensor data from at least a sensor, wherein the at least a sensor comprises an ultrasound sensor;
determining, using the at least a processor, an electrode position within the organ model as a function of the set of sensor data using a position machine-learning module, wherein determining the electrode position comprises:
determining a model position within the organ model as a function of the set of sensor data, wherein determining the model position further comprises:
generating first position training data, wherein the first position training data comprises correlations between exemplary sensor data and exemplary model positions;
training a first position machine-learning model of the position machine-learning module using the first position training data; and
determining the model position within the organ model using the trained first position machine-learning model; and
determining the electrode position within the model position of the organ model as a function of the set of sensor data, wherein determining the electrode position further comprises:
generating second position training data, wherein the second position training data comprises correlations between exemplary sensor data and exemplary electrode positions;
training a second position machine-learning model of the position machine-learning module using the second position training data, wherein the second position training data is iteratively updated on a feedback loop as a function of an output of the first position machine-learning model of the position machine-learning module; and
determining the electrode position within the organ model using the trained second position machine-learning model; and
adding, using the at least a processor, a visual marker onto the electrode position in the model position of the organ model.

### Claim 10 (depends on 9)

The method of claim 9, further comprising:
constructing, using the at least a processor, the organ model as a function of electrocardiogram data using a model machine-learning module.

### Claim 11 (depends on 10)

The method of claim 10, further comprising:
determining, using the at least a processor, a cardiac model machine-learning model from a plurality of model machine-learning models of the model machine-learning module as a function of the electrocardiogram data.

### Claim 12 (depends on 9)

The method of claim 9, wherein the ultrasound sensor comprises an intracardiac echocardiography (ICE) catheter configured to sweep within the organ to generate the set of sensor data as a part of refinement of the organ model.

### Claim 13 (depends on 9)

The method of claim 9, further comprising:
generating, using the at least a processor, a refined organ model as a function of the set of sensor data.

### Claim 14 (depends on 9)

The method of claim 9, wherein the ultrasound sensor comprises an electrode.

### Claim 15 (depends on 9)

The method of claim 9, wherein the first position machine-learning model and the second position machine-learning model comprise a deep neural network.

### Claim 16 (depends on 9)

The method of claim 9, further comprising:
transmitting, using the at least a processor, the visual marker added organ model to a remote device to display the visual marker added organ model to a user.