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Found 4 papers, 1 papers with code
A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification
Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary.
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
The model was fine-tuned to extract relevant features from the ECG images, and then tested on our dataset of CRT patients to predict their response.
A new method using deep learning to predict the response to cardiac resynchronization therapy
The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps.
A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI
Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters.
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