Method and apparatus for predicting pulsed field ablation durability

1. An apparatus for predicting Pulsed Field Ablation (PFA) durability, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to:

receive a training dataset comprising a plurality of example PFA device parameters correlated to a plurality of example PFA outcomes;

train a PFA durability machine learning model using the training dataset;

receive a PFA device parameter; and

generate a PFA durability datum as a function of the PFA device parameter using a trained PFA durability machine learning model.

2. The apparatus of claim 1 , wherein the example PFA outcomes comprise arterial fibrillation (AFib) recurrence data.

3. The apparatus of claim 1 , wherein the example PFA outcomes comprise AFib burden data.

4. The apparatus of claim 1 , wherein the example PFA outcomes comprise historical post-PFA procedure ECG data.

5. The apparatus of claim 4 , wherein the historical post-PFA procedure ECG data comprises historical post-PFA procedure ECG data captured at least 1 day after, 2 days after, 3 days after, 4 days after, 5 days after, 6 days after, 1 week after, 2 weeks after, 3 weeks after, 4 weeks after, 6 weeks after, 2 months after, 3 months after, 6 months after, 9 months after, 1 year after, 2 years after, or 2 years after a historical PFA procedure.

6. The apparatus of claim 1 , wherein:

the training dataset further comprises a plurality of example subject demographic information correlated to the example PFA outcomes; and

the memory contains instructions configuring the at least processor to generate the PFA durability datum as a function of the PFA device parameter and subject demographic information using the trained PFA durability machine learning model.

7. The apparatus of claim 1 , wherein the PFA device parameter is of a type selected from a list consisting of voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and pulse delivery phase.

8. The apparatus of claim 1 , wherein:

the training data further comprises example ECG data;

the memory contains instructions configuring the at least a processor to receive at least an ECG datum; and

the memory contains instructions configuring the at least a processor to generate the PFA durability datum as a function of the PFA device parameter and the at least an ECG datum using the trained PFA durability machine learning model.

9. The apparatus of claim 1 , wherein the training dataset further comprises in-procedure ECG data and in-procedure EGM data.

10. The apparatus of claim 1 , wherein the PFA durability machine learning model comprises a multimodal neural network.

11. A method of predicting Pulsed Field Ablation (PFA) durability, the method comprising:

using at least a processor, receiving a training dataset comprising a plurality of example PFA device parameters correlated to a plurality of example PFA outcomes;

using the at least a processor, training a PFA durability machine learning model using the training dataset;

using the at least a processor, receiving a PFA device parameter; and

using the at least a processor, generating a PFA durability datum as a function of the PFA device parameter using a trained PFA durability machine learning model.

12. The method of claim 11 , wherein the example PFA outcomes comprise arterial fibrillation (AFib) recurrence data.

13. The method of claim 11 , wherein the example PFA outcomes comprise AFib burden data.

14. The method of claim 11 , wherein the example PFA outcomes comprise historical post-PFA procedure ECG data.

15. The method of claim 14 , wherein the historical post-PFA procedure ECG data comprises historical post-PFA procedure ECG data captured at least 1 day after, 2 days after, 3 days after, 4 days after, 5 days after, 6 days after, 1 week after, 2 weeks after, 3 weeks after, 4 weeks after, 6 weeks after, 2 months after, 3 months after, 6 months after, 9 months after, 1 year after, 2 years after, or 2 years after a historical PFA procedure.

16. The method of claim 11 , wherein:

the training dataset further comprises a plurality of example subject demographic information correlated to the example PFA outcomes; and

the PFA durability datum is generated as a function of the PFA device parameter and subject demographic information using the trained PFA durability machine learning model.

17. The method of claim 11 , wherein the PFA device parameter is of a type selected from a list consisting of voltage, pulse duration, frequency, pulse width, amplitude, power of ablation, total energy delivered, total treatment time, energy delivered to a particular location, treatment time at a particular location, current, average power, peak power, and pulse delivery phase.

18. The method of claim 11 , wherein:

the training data further comprises example ECG data;

the method further comprising receiving at least an ECG datum; and

the PFA durability datum is generated as a function of the PFA device parameter and the at least an ECG datum using the trained PFA durability machine learning model.

19. The method of claim 11 , wherein the training dataset further comprises in-procedure ECG data and in-procedure EGM data.

20. The method of claim 11 , wherein the PFA durability machine learning model comprises a multimodal neural network.