Abstract
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.
Highlights
MATERIALS AND METHODSMyocardial perfusion imaging (MPI) is an essential tool for diagnosing and managing patients with suspected or known coronary artery disease [1]
Accuracy standard deviation was lower in K-Nearest Neighbors (KNN), Adaptive Boosting (AB), and Random Forests (RF) (0.06)
F1 measure ranged from 77% (CART) to 89% (SVM) while precision ranged from 79% (CART) to 86% (SVM, AB)
Summary
Myocardial perfusion imaging (MPI) is an essential tool for diagnosing and managing patients with suspected or known coronary artery disease [1]. It is worth to note that sex was not mentioned in the referred GDPR article In this context, we developed ML models to evaluate their ability to distinguish an individual’s sex from assessing myocardial perfusion scintigraphy images. In line with ASNC guidelines [17], all images were obtained from patients that performed ECG-gated 2-day Tc-99 m sestamibi myocardial perfusion single-detector SPECT with R-R signal separated in eight-frame, in rest and stress conditions, and supine position having a total acquisition time of 21 min and 64 projections in a 180◦ orbit. We analyzed 10 attributes related to each summing As it is a supervised learning process, each image was associated with a label indicating the sex of the patient who selected it. The Ethics Committee (Universidade Federal Fluminense) has authorized us to use these images as long as they are anonymized (approval number 91399418.2.0000.5243)
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