US 12,272,068 B2
Predicting atrial fibrillation recurrence after pulmonary vein isolation using simulations of patient-specific magnetic resonance imaging models and machine learning
Natalia A. Trayanova, Baltimore, MD (US); Rheeda Ali, Baltimore, MD (US); and Julie Shade, Baltimore, MD (US)
Assigned to THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed by THE JOHNS HOPKINS UNIVERSITY, Baltimore, MD (US)
Filed on Feb. 6, 2024, as Appl. No. 18/433,704.
Application 18/433,704 is a continuation of application No. 17/425,540, granted, now 11,922,630, previously published as PCT/US2020/015058, filed on Jan. 24, 2020.
Claims priority of provisional application 62/796,855, filed on Jan. 25, 2019.
Prior Publication US 2024/0193782 A1, Jun. 13, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/11 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01)
CPC G06T 7/11 (2017.01) [G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving clinical late gadolinium enhanced cardiac magnetic resonance imaging (LGE-MRI) images of at least one first patient;
performing segmentation of epicardial and endocardial surfaces on the LGE-MRI images to create a three-dimensional (3D) model for the at least one first patient;
identifying normal tissue regions and atrial fibrosis regions in the 3D model;
assigning first cell and tissue properties to the normal tissue regions according to a model of human cardiac myocyte and based on conduction velocities;
assigning second cell and tissue properties to the atrial fibrosis regions according to a model of diffuse fibrosis;
performing simulations on the normal tissue regions and the atrial fibrosis regions, based on the first cell and tissue properties and the second cell and tissue properties, to generate simulation results;
extracting first features from the simulation results based on the 3D model and based on a simulation protocol;
processing, by the device, at least the first features, with a machine learning model, to select a feature that is predictive of atrial fibrillation recurrence; and
utilizing, by the device, the feature to augment the simulation protocol for a second patient.