Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

by   Jeremy Tan, et al.

Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.


Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions

Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left hea...

Segmentation of points of interest during fetal cardiac assesment in the first trimester from color Doppler ultrasound

The present paper puts forward an incipient study that uses a traditiona...

Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views

Accurate geometric quantification of the human heart is a key step in th...

Design Considerations for High Impact, Automated Echocardiogram Analysis

Deep learning has the potential to automate echocardiogram analysis for ...

Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms

Pulmonary hypertension (PH) in newborns and infants is a complex conditi...

Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography

Echocardiography is a powerful prenatal examination tool for early diagn...