An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure.

ABSTRACT:

Aims

Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs.

Methods and results

We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 1

SUBMITTER: Zheng J 

PROVIDER: S-EPMC9448961 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature