Apparatus and method for locating a position of an electrode on an organ model

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.