Artificial Intelligence in the Diagnosis and Management of Arrhythmias

Venkat D. Nagarajan; Su-Lin Lee; Jan-Lukas Robertus; Christoph A. Nienaber; Natalia A. Trayanova; Sabine Ernst

Disclosures

Eur Heart J. 2021;42(38):3904-3916. 

In This Article

Virtual Hearts and Machine Learning in Atrial Fibrillation

Machine learning methodologies in conjunction with atrial computational models were used to define re-entrant driver locations in AF.[80] Segmented LGE CMR scans were used to identify atrial fibrosis in 21 patients with persistent AF. Fibrotic and non-fibrotic regions were identified and were assigned with region-specific tissue properties. Atrial fibrillation was induced using multisite atrial pacing in these virtual atrial models. Phase mapping with an unsupervised density-based spatial cluster algorithm was used to define re-entrant driver locations. Over 80% of re-entrant driver locations matched to the fibrosis border zones.

The first-in-human clinical study of virtual heart models to guide ablation in patients with persistent AF used personalized atrial geometric models created using segmented LGE MR scans done prior to the procedure.[81] Rapid pacing was carried out from 40 uniformly distributed bi-atrial sites. The model response was analysed to determine the optimal ablation lesion set to eliminate all possible persistent re-entrant drivers (identified using above mentioned ML methodologies) sustaining AF and other atrial arrhythmias; the optimal ablation lesion set created using this approach was called OPTIMA: OPtimal Target Identification via Modelling of Arrhythmogenesis was then loaded onto the EAM mapping system and the ablation was carried out without prior mapping. This was a proof of concept feasibility study and was not designed to evaluate procedure outcomes. In fact, outcomes reported from 10 patients were encouraging with no further recurrence of persistent AF.

In a study combining ML and personalized computational modelling, an ML algorithm was shown to predict AF recurrence post-pulmonary vein isolation in patients with paroxysmal AF.[82] In this proof of concept study, features were derived from patient's pre-pulmonary vein isolation LGE MR images and also from the results of AF simulations carried out on their personalized computational model. Random forests were used for unbiased feature selection, and ten-fold nested cross-validation was used to train, validate, and test quadratic discriminant analysis ML classifier. Most predictive features were used as input to this classifier. This ML algorithm predicted post-pulmonary vein isolation AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation AUC of 0.82.

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