Neural-network classification of cardiac disease from 31 P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism

J Cardiovasc Magn Reson. 2019 Aug 12;21(1):49. doi: 10.1186/s12968-019-0560-5.


Background: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive phosphorus (31P) cardiovascular magnetic resonance spectroscopy (CMRS) measurements of cardiac adenosine triphosphate (ATP) energy, phosphocreatine (PCr), the first-order CK reaction rate kf, and the rate of ATP synthesis through CK (CK flux), can predict specific human heart disease and clinical severity.

Methods: The data comprised the extant 178 complete sets of PCr and ATP concentrations, kf, and CK flux data from human CMRS studies performed on clinical 1.5 and 3 Tesla scanners. Healthy subjects and patients with nonischemic cardiomyopathy, dilated (DCM) or hypertrophic disease, New York Heart Association (NYHA) class I-IV heart failure (HF), or with anterior myocardial infarction are included. Three-layer neural-networks were created to classify disease and to differentiate DCM, hypertrophy and clinical NYHA class in HF patients using leave-one-out training. Network performance was assessed using 'confusion matrices' and 'area-under-the-curve' (AUC) analyses of 'receiver operating curves'. Possible methodological bias and network imbalance were tested by segregating 1.5 and 3 Tesla data, and by data augmentation by random interpolation of nearest neighbors, respectively.

Results: The network differentiated healthy, HF and non-HF cardiac disease with an overall accuracy of 84% and AUC > 90% for each category using the four CK metabolic parameters, alone. HF patients with DCM, hypertrophy, and different NYHA severity were differentiated with ~ 80% overall accuracy independent of CMRS methodology.

Conclusions: While sample-size was limited in some sub-classes, a neural network classifier applied to noninvasive cardiac 31P CMRS data, could serve as a metabolic biomarker for common disease types and HF severity with clinically-relevant accuracy. Moreover, the network's ability to individually classify disease and HF severity using CK metabolism alone, implies an intimate relationship between CK metabolism and disease, with subtle underlying phenotypic differences that enable their differentiation.

Trial registration: Identifier: NCT00181259.

Keywords: Biomarker; Cardiac metabolism; Heart failure; Neural network; Phosphorus spectroscopy; Translational studies.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenosine Triphosphate / metabolism
  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers / blood
  • Creatine Kinase / metabolism*
  • Energy Metabolism*
  • Female
  • Heart Diseases / classification
  • Heart Diseases / diagnosis*
  • Heart Diseases / enzymology
  • Humans
  • Kinetics
  • Machine Learning*
  • Magnetic Resonance Spectroscopy*
  • Male
  • Middle Aged
  • Myocardium / enzymology*
  • Neural Networks, Computer*
  • Phosphocreatine / metabolism
  • Phosphorus Isotopes
  • Predictive Value of Tests
  • Reproducibility of Results
  • Severity of Illness Index
  • Young Adult


  • Biomarkers
  • Phosphorus Isotopes
  • Phosphocreatine
  • Adenosine Triphosphate
  • Creatine Kinase

Associated data