Potential of Machine Learning
HFpEF is heterogeneous entity affected by multiple clinical variables
and structural alterations. Current methods resort to an oversimplified
approach for assessment and risk stratification 4.
This one size fits all approach has not proved applicable in clinical
practice. With the evolution of technology and computer capabilities,
artificial intelligence (AI) is opening new frontiers in cardiovascular
imaging. Machine learning (ML), a subset of AI, is ushering a new era in
cardiovascular imaging by expanding boundaries not limited by
conventional statistics 49. Unlike traditional
approaches, ML can decipher hidden patterns and extrapolate hidden
patterns within vast data matrices 50,51. This
technology can integrate diastolic indices and speckle tracking
echocardiography to offer innovative insights into HFpEF52 (Figure 4).
Lancaster et al performed an unsupervised, hierarchical cluster analysis
of 866 patients with diastolic dysfunction graded using contemporary ASE
recommendations 53. Major adverse cardiovascular
events, hospitalization, and mortality were compared between
conventional and cluster-based categorizations. Survival analyses of
patients assessed by clustering algorithms showed improved prediction of
event-free survival by cluster analysis over diastolic grade
classifications for all-cause mortality and cardiac mortality.
Omar et al performed a cluster analysis of LA and LV mechanical
deformation parameters that resulted in a Doppler-independent phenotypic
characterization of diastolic function and provided a noninvasive
estimation of LV filling pressures 54. Speckle
tracking features independently clustered patients into three groups
with conventional parameters verifying increasing severity of
dysfunction and LV filling pressure. Subsequent investigations from the
same group lead to a refined ML model for assessing LV filling pressure
using fourteen speckle tracking variables 55. This
model correctly identified 80% of patients with pulmonary capillary
wedge pressure ≥18 mm Hg,
ML has also been applied to resting and stress deformation imaging.
Tabassian et al explored the role of ML in analyzing LV long axis
mechanics during stress and rest in 100 patients including those with
HFpEF and healthy, hypertensive, and breathless control
subjects56. A ML algorithm was used to model
spatiotemporal patterns of the speckle tracking traces and compare the
ML algorithm predictions with the clinical diagnoses. The ML algorithm
predicted symptoms with a high degree of accuracy and assigning subjects
into four phenotypic groups. ML incorporating strain rate, compared with
standard measurements, provided the greatest improvement in accuracy for
predicting symptoms and 6-min walk distance. Sanchez-Martinez et al also
utilized measurements of LV deformation at rest and exercise to examine
differences between HFpEF and healthy patients from the MEDIA study
(MEtabolic road to DIAstolic heart failure) 57. LV
long-axis myocardial velocity patterns analyzed using an unsupervised ML
algorithm identified a continuum from health to disease, including a
transition zone associated with an uncertain diagnosis. As we move
forward in the current era of cardiovascular imaging, ML algorithms will
be increasingly integrated into clinical practice and cardiovascular
research. These methods may help detect previously unrecognized
phenotypes and tailor individualized therapies 4.