Real time processing of Fetal Echocardiography using Deep Learning For Congenital Heart Defect

Fetal echocardiography (fetal echo) is an ultrasonic procedure conducted during pregnancy to measure the unborn baby’s heart. Referral for fetal echo typically occurs between 18 and 22 weeks gestational age. With advances in ultrasound technology, which has allowed for the significant improvements in high‐resolution imaging necessary to visualize the condition of congenital heart disease (CHD) (Pike et al.,2014). CHD diagnoses in the first trimester were first reported in the early 1990s, using transvaginal ultrasound (Bronshtein et al., 1991). CHD is a general term for a structural or functional defect of the heart that is present at birth. The heart is a complex organ formed from cells derived from at least four distinct progenitor cell types (Smith et al., 2020).

The Intelligent Systems Research Group (ISysRG) has implemented real time processing of Fetal Echocardiography using Deep Learning for Congenital Heart Defect.  YOLO v4 and the Faster R-CNNs architecture are implemented for object detection algorithm. This research was supported by public video of fetal heart (.mp4 format). Based on YOLO v4 architecture, we detected chambers of fetal heart, i.e., left atrial, right atrial, left ventricle, right ventricle, and aorta. Meanwhile, the Faster-RCNN algorithm is performed to detect four chambers, Right Ventricular Outflow Tract (RVOT), Light Ventricular Outflow Tract (LVOT), and 3 vessels.

Video 1

Video 2

YOLO v4: Real-Time Object Detection

Figure 1. Faster-RCNN algorithm is performed to detect four chambers, Right Ventricular Outflow Tract (RVOT), Light Ventricular Outflow Tract (LVOT), and 3 vessels.

Tags: No tags

2 Responses

Add a Comment

Your email address will not be published. Required fields are marked *