A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring.

ABSTRACT:

Objective

Non-invasive methods for hemoglobin (Hb) monitoring can provide additional and relatively precise information between invasive measurements of Hb to help doctors' decision-making. We aimed to develop a new method for Hb monitoring based on mask R-CNN and MobileNetV3 with eye images as input.

Methods

Surgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extracted, respectively, from the two kinds of images were used as inputs. A combination of feature engineering and regression, solely MobileNetV3, and a combination of mask R-CNN and MobileNetV3 were applied for model development. The model's performance was evaluated using metrics such as R²,

SUBMITTER: Hu XY 

PROVIDER: S-EPMC10435289 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature