Mechanical Ventilator Settings Estimation From an <scp>AI</scp> Model

ABSTRACT Mechanical ventilation (MV) is used in subjects with respiratory problems for assistance in breathing. Various MV settings are adjusted at the clinician's discretion based on the patient's respiratory condition. In this study, an AI (artificial intelligence) model using artificial neural networks (ANNs) along with Bayesian Optimization (BO) was developed to estimate the desired MV settings for various subject scenarios. The ANN model with two hidden layers was trained with experimental data collected from subjects (canines and felines) in our previous work. Inverse mapping of the trained ANNs was conducted using BO to predict the acceptable MV settings for specific subject outcomes. Our results suggest that the model can support veterinarians in estimating the proper MV parameters for optimal subject outcome.