AUTHOR=de Souza Filho Erito Marques , Fernandes Fernando de Amorim , Wiefels Christiane , de Carvalho Lucas Nunes Dalbonio , dos Santos Tadeu Francisco , dos Santos Alair Augusto Sarmet M. D. , Mesquita Evandro Tinoco , Seixas Flávio Luiz , Chow Benjamin J. W. , Mesquita Claudio Tinoco , Gismondi Ronaldo Altenburg TITLE=Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps JOURNAL=Frontiers in Cardiovascular Medicine VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.741667 DOI=10.3389/fcvm.2021.741667 ISSN=2297-055X ABSTRACT=

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.