Classification of ischemia from myocardial polar maps in ¹⁵O-H₂O cardiac perfusion imaging using a convolutional neural network.

ABSTRACT: We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from ¹⁵O-H₂O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0

SUBMITTER: Teuho J 

PROVIDER: S-EPMC8857225 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature