Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.

ABSTRACT:

Background

The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs.

Materials and methods

This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with th

SUBMITTER: Yang Q 

PROVIDER: S-EPMC7262550 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature