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.

Prophylactic antibiotic usage during delivery is a common practice worldwide, especially in low- to middle-income countries. Guidelines have been published to reduce antibiotic overuse; however, data describing the use of prophylactic antibiotics and clinician adherence to guidelines in low- to middle-income countries remain limited. This study aimed to describe the prevalence of prophylactic antibiotic use, factors associated with its use, and clinician adherence to guidelines. A retrospective review was conducted for all deliveries from 1 January 2016 to 31 December 2018 at a tertiary level hospital in Indonesia. The prevalence of prophylactic antibiotic use during delivery was 47.1%. Maternal education level, Ob/Gyn specialist-led delivery, a history of multiple abortions, C-section, premature membrane rupture, and antepartum hemorrhage were independently associated with prophylactic antibiotic use. Clinician adherence to the guidelines was 68.9%. Adherence to guidelines was the lowest in conditions where the patient had only one indication for prophylactic antibiotics (aOR 0.36, 95% CI 0.24–0.54). The findings showed that the prevalence of prophylactic antibiotic use during delivery was moderate to high. Adherence to local guidelines was moderate. Updating the local prescribing guidelines may improve clinician adherence.

Probiotics have been known for their use in medical field for quite a long time. Strong evidences are now available for the use of probiotics in clinical setting. One of the current issues on this topic is the use of probiotics in pregnancy. Recent studies showed that probiotics may be safe and beneficial for prenatal supplementation. In this review, we highlighted several proven use of probiotics supplementation in pregnant women. A few selected strains of probiotics showed promising outcome to prevent preterm labor and preeclampsia, and to reduce atopic eczema but not asthma and wheezing, in offspring of women who had prenatal probiotics supplementation. The mechanism of action responsible for this effect is closely related to the regulation of T cells, although the exact pathways are not defined yet.

Early prenatal screening with an ultrasound (US) can significantly lower newborn mortality caused by congenital heart diseases (CHDs). However, the need for expertise in fetal cardiologists and the high volume of screening cases limit the practically achievable detection rates. Hence, automated prenatal screening to support clinicians is desirable. This paper presents and analyses potential deep learning (DL) techniques to diagnose CHDs in fetal USs. Four convolutional neural network architectures were compared to select the best classifier with satisfactory results. Hence, dense convolutional network (DenseNet) 201 architecture was selected for the classification of seven CHDs, such as ventricular septal defect, atrial septal defect, atrioventricular septal defect, Ebstein’s anomaly, tetralogy of Fallot, transposition of great arteries, hypoplastic left heart syndrome, and a normal control. The sensitivity, specificity, and accuracy of the DenseNet201 model were 100%, 100%, and 100%, respectively, for the intra-patient scenario and 99%, 97%, and 98%, respectively, for the inter-patient scenario. We used the intra-patient DL prediction model to validate our proposed model against the prediction results of three expert fetal cardiologists. The proposed model produces a satisfactory result, which means that our model can support expert fetal cardiologists to interpret the decision to improve CHD diagnostics. This work represents a step toward the goal of assisting front-line sonographers with CHD diagnoses at the population level.

Placenta accreta spectrum (PAS) is characterized by abnormal invasion of placental tissue into the underlying uterine muscles and has an incidence of 1/533–1/251. The incidence of complications includes uterine rupture (14–29%), PAS (6–10%), and retained placenta or incomplete placenta removal (4%). Here, we described a rare case of PAS and angular pregnancy, including how to diagnose and manage it preoperatively. A 32-year-old primigravida diagnosed at 24 weeks of gestation with a right angular pregnancy was admitted due to preterm premature rupture of membrane (PPROM) with a singleton fetus. We decided to perform hysterotomy because of the PPROM and intrauterine infection. Intraoperatively, we found PAS in the right angular pregnancy; therefore, we performed uterine conservative management with wedge resection on the right uterine fundus. Intraoperative bleeding was 1,600 cc. Histopathological examination revealed placenta increta. The maternal prognosis was good, while the fetus was poor, with an APGAR score of 1/1/0.