Detection of Fetal Cardiac Chamber Three Vessel Trachea View using Deep Learning

In fetal cardiology, echocardiographic imaging has been shown to aid in the diagnosis of fetal monitoring for the development of fetal heart function. The small heart size and the lack of expertise in fetal echocardiography of some sonography pose challenges in terms of research. The use of technology to enhance the image assists in the diagnosis of abnormalities important for optimal fetal cardiac assessment. Multi object detection is the most challenging but very informative task in basic machine learning. We must detect cardiac substructure precisely the condition of the fetal heart, especially in normal conditions. Knowing the normal heart substructure can provide information about the presence or absence of fetal heart abnormalities. Multiple ways to view fetal heart chambers, four chambers (4CH) basic standard cardiac scan, left and right ventricular outflow tract (LVOT/RVOT) focusing on structural pathways, and Three Tracheal Vessels (3VV/3VT) complex fetal heart scan confirms flow in the three blood vessels helps determine whether aortic arch problems are causing normal heart conditions or not. The ductus arteriosus (DUCT), ascending aorta (AoA), superior vena cava (SVC), and trachea. In particular, the framework performs detection using a deep learning detection model applied to 3VT ultrasound images. This study identifies the evaluation using the mean average precision (mAP). The results of the study detecting the normal condition of the fetal heart substructure gave good results on the results of the mAP evaluation of 87.55% and each object of DUCT, AoA, SVC, Trachea with AP values of 92%, 91%, 91%, 76%.