Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images.

ABSTRACT: Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images. Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model. Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81-0.85 g/dl) and the AUC of the screen

SUBMITTER: Zhao X 

PROVIDER: S-EPMC9160874 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature