A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.

ABSTRACT:

Background

Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI).

Methods

We collected surgical patients' non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MS

SUBMITTER: Li YJ 

PROVIDER: S-EPMC7607084 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature