Master Frame Extraction of Fetal Cardiac Images Using B Mode Ultrasound Images

Fetal Echocardiography is used for monitoring the fetal heart and for detection of Congenital Heart Disease (CHD). It is well known that fetal cardiac four chamber view has been widely used for preliminary examination for the detection of CHD. The end diastole frame is generally used for the analysis of the fetal cardiac chambers which is manually picked by the clinician during examination/screening. This method is subjected to intra and inter observer errors and also time consuming. The proposed study aims to automate this process by determining the frame, referred to as the Master frame from the cine loop sequences that can be used for the analysis of the fetal heart chambers instead of the clinically chosen diastole frame. The proposed framework determines the correlation between the reference (first) frame with the successive frames to identify one cardiac cycle. Then the Master frame is formed by superimposing all the frames belonging to one cardiac cycle. The master frame is then compared with the clinically chosen diastole frame in terms of fidelity metrics such as Dice coefficient, Hausdorff distance, mean square error and structural similarity index. The average value of the fidelity metrics considering the dataset used for this study 0.73 for Dice, 13.94 for Hausdorff distance, 0.99 for Structural Similarity Index and 0.035 for mean square error confirms the suitability of the proposed master frame extraction thereby avoiding manual intervention by the clinician. .

Prenatal Diagnosis of Aortic Coarctation: Prediction Algorithm according to Gestational Age

<b><i>Objectives:</i></b> The aim of the study was first to quantify the diagnostic accuracy of predictive anatomical factors of aortic coarctation (CoA) and second to design a postnatal CoA probability algorithm according to gestational age (GA) in prenatal period. <b><i>Methods:</i></b> Global and according to GA diagnostic performance of cardiac anatomical variables using the ROC curve were evaluated in a retrospective cohort of fetuses with suspicion of CoA (2004–2020). A serial testing strategy to predict postnatal CoA by fetal echocardiography was designed. <b><i>Results:</i></b> 114 fetuses were included. Isthmus-to-ductal (I/D) ratio provided the best discrimination between healthy fetuses and those with CoA (AUC 0.91, 95% CI: 0.86–0.96, I/D &#x3c; 0.74 sensitivity 96.3%, I/D &#x3c; 0.6, specificity 92.5%) with good classification capacity in both the second and third trimesters of gestation. Isthmus <i>z</i>-score and pulmonary/aortic valve ratio increased accuracy in fetuses &#x3e;28 and tricuspid/mitral valve ratio (TV/MV) in fetuses ≤28 weeks. Study of I/D plus TV/MV ratio in fetuses ≤28 and I/D ratio plus isthmus <i>z</i>-scores in fetuses &#x3e;28 weeks allowed to correctly classify 91.8% of fetuses as high or low probability of postnatal CoA. <b><i>Conclusions:</i></b> Diagnostic discrimination of anatomic predictive factors for CoA varies according to GA. Specific algorithms according to GA increase accuracy in CoA’s prenatal prediction.

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

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.

Congenital Complete Heart Block in Fetal Echocardiography

Congenital Complete Heart Block (CHB) in a fetus is a rare diagnosis occurring 1 in 15,000 to 20,000 live births which can be diagnosed by echocardiography. It’s less talked disease. It is uncommonly reported even in countries where fetal echocardiography is performed in much higher numbers. For countries like Nepal, where small number of fetal echocardiography is performed, we present a case of lupus as the cause of congenital CHB.

Prenatal Diagnosis of Ebstein Anomaly in a Centre with Limited Resources: A Potential Pitfall

BACKGROUND: Ebstein anomaly is a rare congenital cardiac malformation characterized by varying degrees of downward displacement of the tricuspid valve leaflets into the cavity of the right ventricle. The pathophysiology varies with the degree of valvular malfunction, the size, and contractility of the right ventricle, and the presence of obstruction within the right ventricular outflow tract. An early diagnosis makes worse the often prognosis. Fetal echocardiography is a useful tool for the diagnosis of congenital heart disease including the Ebstein anomaly which shows a wide spectrum of ultrasonographic manifestations and, sometimes it can be difficult to make a true prenatal diagnosis. CASE REPORT: Here, we describe a unique case of an Ebstein anomaly in 26 weeks of gestation fetus through prenatal echocardiographic evaluation. Fetus was suspected as a severe form of the Ebstein anomaly, with a severe form of right atrium enlargement, huge tricuspid insufficiency, and hypoplastic pulmonary artery. The atrial septum, dimensions of cardiac structures, left and right cardiac output, and Doppler interrogation of cardiac flows were examined. CONCLUSION: This case study presents a well-documented case of Ebstein’s anomaly type D that was diagnosed prenatally using fetal echocardiography.