Accuracy of the Standardized Early Fetal Heart Assessment in Excluding Major Congenital Heart Defects in High‐Risk Population

2021 ◽  
Author(s):  
Sifa Turan ◽  
Mehmet Resit Asoglu ◽  
Halis Ozdemir ◽  
Lindsey Seger ◽  
Ozhan Mehmet Turan
Keyword(s):  
High Risk ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Fetal Heart ◽  
Heart Defects ◽  
High Risk Population ◽  
Risk Population

scholarly journals OP12.09: Congenital heart defects at 11-16 weeks in a high risk population

2012 ◽  
Vol 40 (S1) ◽  
pp. 92-92
Author(s):  
M. Bennasar ◽  
J. Martinez ◽  
N. Masoller ◽  
O. Gómez ◽  
F. Crispi ◽  
...  
Keyword(s):  
High Risk ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Heart Defects ◽  
High Risk Population ◽  
Risk Population

657: Intrapartum fetal heart rate characteristics of fetuses with trisomy 21 and congenital heart defects

2011 ◽  
Vol 204 (1) ◽  
pp. S259-S260
Author(s):  
Priyadarshini Koduri ◽  
Maria Adelaida Giraldo ◽  
Phillip Shlossman ◽  
Anthony Sciscione ◽  
Vincenzo Berghella ◽  
...  
Keyword(s):  
Heart Rate ◽  
Congenital Heart Defects ◽  
Fetal Heart Rate ◽  
Trisomy 21 ◽  
Congenital Heart ◽  
Fetal Heart ◽  
Heart Defects

scholarly journals The diagnostic value of the early extended fetal heart examination at 13 to 14 weeks gestational age in a high-risk population

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Baoying Ye ◽  
Yi Wu ◽  
Jiong Chen ◽  
Yu Yang ◽  
Jianmei Niu ◽  
...  
Keyword(s):  
High Risk ◽  
Gestational Age ◽  
Fetal Heart ◽  
Diagnostic Value ◽  
High Risk Population ◽  
Risk Population

scholarly journals Maternal biomarkers for fetal heart failure in fetuses with congenital heart defects or arrhythmias

2019 ◽  
Vol 220 (1) ◽  
pp. 104.e1-104.e15 ◽  
Author(s):  
Takekazu Miyoshi ◽  
Hiroshi Hosoda ◽  
Michikazu Nakai ◽  
Kunihiro Nishimura ◽  
Mikiya Miyazato ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Fetal Heart ◽  
Heart Defects ◽  
Fetal Heart Failure

scholarly journals OC213: Early diagnosis of fetal congenital heart disease in a high risk population

2003 ◽  
Vol 22 (S1) ◽  
pp. 57-57
Author(s):  
J. Martinez ◽  
O. G�mez del Rinc�n ◽  
M. del R�o ◽  
F. Figueras ◽  
V. Borobio ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
High Risk ◽  
Early Diagnosis ◽  
Congenital Heart ◽  
High Risk Population ◽  
Risk Population

scholarly journals Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8007
Author(s):  
Siti Nurmaini ◽  
Muhammad Naufal Rachmatullah ◽  
Ade Iriani Sapitri ◽  
Annisa Darmawahyuni ◽  
Bambang Tutuko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Fetal Heart ◽  
Fetal Echocardiography ◽  
Heart Defects ◽  
Disease Diagnosis ◽  
Detection Techniques ◽  
Accuracy Of Measurements ◽  
Computer Aided

Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans’ training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.

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