Imaging Heart Failure With Artificial Intelligence
Improving the Realism of Synthetic Wisdom
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See Article by Sanchez-Martinez et al
The technological origami is unfolding before our eyes; each leaf that unfurls bestows us with new challenges and opportunities. During the last 2 decades, several modalities in cardiovascular imaging have evolved with the sheer number of parameters utilized in the assessment of cardiac function. Particularly, the assessment of cardiac motion and deformation from echocardiography and magnetic resonance imaging creates a large volume of spatially and temporally diverse data. In the absence of suitable techniques, the majority of the data analysis currently is time constrained by graph-based assessments of spatially averaged functional data over a cardiac cycle. Computational modeling and the emerging algorithmic objectivity in machine learning can provide opportunities to resolve such dilemmas and minimize impediments that arise from high-dimensional imaging datasets. To this end, Sanchez-Martinez et al,1 in this issue of Circulation: Cardiovascular Imaging, proposed the adoption of algorithmic objectivity to differentiate patients with heart failure with preserved ejection fraction from those who suffered dyspnea from other causes. This work extends the potential approaches suggested in previous investigations toward automation and acceleration of complex pattern recognition using cardiac imaging data for disease phenotyping.2
Utilizing the data from MEDIA study (Metabolic Road to …