	US 12,213,774 B1
	Apparatus and method for locating a position of an electrode on an organ model
Murali Aravamudan, Andover, MA (US); Rakesh Barve, Bengaluru (IN); Suthirth Vaidya, Bengaluru (IN); Uddeshya Upadhyay, Bengaluru (IN); Abhijith Chunduru, Bengaluru (IN); Arjun Puranik, San Jose, CA (US); and Sai Saketh Chennamsetty, Bengaluru (IN)
Assigned to nference, Inc., Cambridge, MA (US)
Filed by nference, Inc., Cambridge, MA (US)
Filed on Jan. 2, 2024, as Appl. No. 18/402,124.
Int. Cl. A61B 5/06 (2006.01); A61B 8/00 (2006.01); A61B 8/08 (2006.01); A61B 8/12 (2006.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)

CPC A61B 5/066 (2013.01) [A61B 8/0841 (2013.01); A61B 8/0883 (2013.01); A61B 8/12 (2013.01); A61B 8/4245 (2013.01); A61B 8/5261 (2013.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)]

16 Claims

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.