One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

Background: Anxiety and depression during the antenatal period is a growing problem with major effects on the mother, the developing fetus, and the neonate. Objectives: To assess the prevalence of anxiety and depression during pregnancy in women attending the hospital for antenatal care and assess the associated factors. Methods: This is a prospective cohort study conducted in the University Hospital of Imam Abdulrahman Bin Faisal University. Anxiety was evaluated using State Trait Anxiety Inventory. Depression was assessed using Edinburgh Postnatal Depression Scale (EPDS). Results: Complete data were available for 575 women. The mean EPDS score was 10.5 (SD 5.5). The prevalence of depression was 26.8%. The mean state-anxiety score was 38.4 (SD 11.4) and mean trait-anxiety score was 38.2 (SD 9.5). The prevalence of anxiety using state-anxiety scale was 23.6%, while using the trait scale it was 23.9%. The risk is higher among unemployed women with history of miscarriage and unplanned pregnancy. Conclusion: Anxiety and depression are common during pregnancy.

Background: The presence of group B streptococcus in the genital area during pregnancy and labor is associated with high neonatal morbidity and mortality. However, the exact prevalence of group B streptococcus among Saudi women has not yet been established. Objective: The aim of this study was to determine the prevalence of group B streptococcal colonization in Saudi pregnant women as a primary end-point and neonatal complications as a secondary end-point. Materials and Methods: A prospective, observational, cross-sectional study was conducted to estimate the prevalence of group B streptococcal colonization among Saudi women admitted in labor to the King Fahd Hospital of the University, Al-Khobar, Saudi Arabia. A total of 1371 maternal specimens (vaginal swabs, rectal swabs and midstream urine) were collected from 457 patients between October 2011 and September 2016. Neonatal specimens (urine, blood and cerebrospinal fluid) were collected if clinically indicated. Results: Of the 457 women enrolled in this study, 87 (19%) had positive cultures for group B streptococcus either in the vaginal or rectal swab or both. Group B streptococcus was also found to be the most commonly isolated organism. In total, there were five cases of neonatal sepsis, of which one early-onset neonatal sepsis was caused by group B streptococcus. Conclusions: This study found that the prevalence of group B streptococcal colonization is 19% among Saudi women admitted in labor to the King Fahd Hospital of the University.

Amniotic Fluid (AF) refers to a protective liquid surrounding the fetus inside the amniotic sac, serving multiple purposes, and hence is a key indicator of fetal health. Determining the AF levels at an early stage helps to ascertain the maturation of lungs and gastrointestinal development, etc. Low AF entails the risk of premature birth, perinatal mortality, and thereby admission to intensive care unit (ICU). Moreover, AF level is also a critical factor in determining early deliveries. Hence, AF detection is a vital measurement required during early ultrasound (US), and its automation is essential. The detection of AF is usually a time-consuming process as it is patient specific. Furthermore, its measurement and accuracy are prone to errors as it heavily depends on the sonographer’s experience. However, automating this process by developing robust, precise, and effective methods for detection will be beneficial to the healthcare community. Therefore, in this paper, we utilized transfer learning models in order to classify the AF levels as normal or abnormal using the US images. The dataset used consisted of 166 US images of pregnant women, and initially the dataset was preprocessed before training the model. Five transfer learning models, namely, Xception, Densenet, InceptionResNet, MobileNet, and ResNet, were applied. The results showed that MobileNet achieved an overall accuracy of 0.94. Overall, the proposed study produces an effective result in successfully classifying the AF levels, thereby building automated, effective models reliant on transfer learning in order to aid sonographers in evaluating fetal health.

Retained placenta is clinically diagnosed when the placenta has failed to deliver within 18 to 60 minutes after birth. The retained placenta is a risk factor for postpartum fever. In this paper, we report a patient with a delivery complicated by a retained placenta and postpartum fever. This patient, a 34-year-old pregnant female, was admitted at 32 weeks of gestation for a case of preterm labor with preterm premature rupture of membranes and bacterial vaginosis. A 2.5 kg infant was delivered by normal vaginal delivery, which was followed by active management of the third stage of labor. The retained placenta was removed manually under general anesthesia. Two days later, the patient developed a fever and elevated inflammatory markers. Ultrasound-guided evacuation and curettage were done, and two endometrial cavities were noted. Both cavities were evacuated of products of conception. Two days later, the patient started to have spikes of fever. Imaging revealed an intra-cavity soft tissue mass measuring 6.5 cm. Hysteroscopy with dilation and curettage was performed and showed fibrous bands covering a soft mass of products of conception, which was then evacuated.

A 31-year-old nulliparous woman presented to the emergency room with history of excessive bleeding for a few months. She also had urine retention for 1 day. On examination, she was pale. Vaginal examination showed a large mass distending the vagina with foul smell and moderate bleeding. Her haemoglobin was 6.2 g/L. Pelvic ultrasound scan was not conclusive in ascertaining the origin of the mass. MRI revealed the presence of a pedunculated, large (6×7×12 cm) submucous fibroid (figure 1). The fibroid pedicle was broad and attached to the fundus. The uterine fundus was inverted in the uterine cavity. The cervix was dilated due to the large fibroid. There was also degeneration and haemorrhagic foci in the fibroid. Figure 2 shows a schematic presentation of the uterus and fibroid on MRI image.