Building a machine learning-assisted echocardiography prediction tool for children at risk for cancer therapy-related cardiomyopathy.

ABSTRACT:

Background

Despite routine echocardiographic surveillance for childhood cancer survivors, the ability to predict cardiomyopathy risk in individual patients is limited. We explored the feasibility and optimal processes for machine learning-enhanced cardiomyopathy prediction in survivors using serial echocardiograms from five centers.

Methods

We designed a series of deep convolutional neural networks (DCNNs) for prediction of cardiomyopathy (shortening fraction ≤ 28% or ejection fraction ≤ 50% on two occasions) for at-risk survivors ≥ 1-year post initial cancer therapy. We built DCNNs with four subsets of echocardiographic data differing in timing relative to case (survivor who developed cardiomyopathy) index diagnosis and two input formats (montages) with differing image sele

SUBMITTER: Edwards LA 

PROVIDER: S-EPMC11462765 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature