Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.

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.

Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging.

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.

Diagnostic performance of combined cardiac MRI for detection of coronary artery disease.

OBJECTIVE: To evaluate the diagnostic performance of stress perfusion cardiac MR (CMR) for detecting significant CAD (≥70% narrowing) in comparison with invasive coronary angiography (ICA) as a reference standard. METHODS: Examinations of 54 patients who underwent both stress perfusion CMR and ICA for investigation of CAD between 2007 and 2009 were evaluated. The CMR protocol included dipyridamole stress and rest perfusion, stress and rest cine MRI for assessment of ventricular function and delayed gadolinium enhancement for assessment of myocardial viability and detection of infarction. CMR interpretation was performed by 2 observers blinded to the results of ICA and the clinical history. RESULTS: From a total of 54 patients, 37 (68.5%) showed significant CAD in 71 coronary territories. A perfusion defect was detected in 35 patients and in 69 coronary territories. Individual stress perfusion CMR evaluation showed the highest accuracy (83%) of the CMR techniques. The combined analysis using all sequences increased the overall accuracy of CMR to 87%. CONCLUSION: Combination of perfusion and cine-MR during stress/rest, associated to delayed enhancement in the same protocol improves CMRI diagnostic accuracy and sensitivity for patients with significant coronary stenosis, and may therefore be helpful for risk stratification and defining treatment strategies.