Cardiac resynchronization therapy (CRT) has been a significant step forward in the
treatment of patients with arrhythmias leading to heart failure. However, it still
presents two main issues. Firstly, the criteria to be a candidate for CRT are too
simplistic, based on basic indices. Second, the therapy still has a 30% failure rate.
Several studies have concluded that specific myocardial motion patterns, mainly the
septal flash (SF), are related to a higher success rate of CRT. In prior works ...
Cardiac resynchronization therapy (CRT) has been a significant step forward in the
treatment of patients with arrhythmias leading to heart failure. However, it still
presents two main issues. Firstly, the criteria to be a candidate for CRT are too
simplistic, based on basic indices. Second, the therapy still has a 30% failure rate.
Several studies have concluded that specific myocardial motion patterns, mainly the
septal flash (SF), are related to a higher success rate of CRT. In prior works iden-
tifying SF in left bundle branch block (LBBB) patients, a dyssynchrony abnormal
pattern was present in 45% of the total patients. Its proper identification could serve
both as a predictor of CRT success and as a more accurate condition to look for
in CRT candidates, since SF is being detected manually by experienced clinicians
these days, leading to high inter-observer variability.
Most approaches to distinguish abnormal conditions rely on medical data. There-
fore, machine learning and, concretely, neural networks can be key to revealing new
patterns of deformation and improving the characterization and quantification of
myocardial motion problems. These facts make SF identification a great subject
of study for neural networks, thus potentially impacting clinical practice. However,
septal flash occurs at a very specific point of time-space during the cardiac cycle.
While neural networks have been demonstrated to be extremely accurate at finding
such patterns, they have a huge lack of interpretability, a mandatory feature for clin-
ical applications. In this study, I propose a method to analyze 2D ultrasound video
sequences of cardiac motion and automatically characterize septal flash in an inter-
pretable way. This is achieved by defining a time-distributed neural network with an
attention system based on gradient class activation maps. The system identifies the
most critical frames on the sequence where SF appears and marks the spatial region
where the deformation takes place. The final project had 300 available sequences,
241 train and 59 test, and achieved a maximum accuracy of 0.80.
+
