Deep learning to predict esophageal variceal bleeding based on endoscopic images.

ABSTRACT:

Objective

Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images.

Methods

Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists.

Results

In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintaine

SUBMITTER: Hong Y 

PROVIDER: S-EPMC10566287 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature