Case Western Reserve University

The Cleveland Clinic Foundation

Mina Chung

Anant Madabhushi

Thomas Atta-Fosu

Marjan Firouznia

Albert Feeny

Michael LaBarbera

Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.

Fractal analysis of left atrium to predict atrial fibrillation recurrence

Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

Prediction of risk of post-ablation atrial fibrillation based on radiographic features of pulmonary vein morphology from chest imaging

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Shaker Heights, OH, United States of America

Mina Chung