Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

ABSTRACT:

Objective

To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.

Methods

All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class ac

SUBMITTER: Walker MC 

PROVIDER: S-EPMC9216531 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature