Background. Antenatal depression (AND) is a common mood disorder that affects both the mother and the child. Objective. The current study is aimed at identifying the prevalence of antenatal depression and the risk factors associated with it in South Indian pregnant women. Materials and Methods. The current study was carried out in a tertiary care teaching hospital where pregnancy and postnatal care are offered. In the study, 314 pregnant women who visited the antenatal clinic for their prenatal checkups were included. To diagnose possible depression, Edinburgh Postnatal Depression Scale (EPDS) was used. The chi-square test was applied to determine the association between antenatal depression and various socioeconomic, obstetric, and medical factors. A logistic regression analysis was performed to identify significant confounding variables. Results. Of the total 314 women, 69 (21.98%) were suffering from possible depression with the mean EPDS score being . Women of younger age had greater risks for depression than older women ( ; 95% CI: 0.56-7.20). Maternal age ( , ) and the presence of health issues during the current pregnancy ( , ) were the factors significantly associated with antenatal depression. Conclusions. Clinical efforts should focus on screening antenatal depression, early identification, and effective care, thus preventing progression to postpartum depression and its detrimental effects.

Background. Amniotic fluid index (AFI) is one of the major and deciding components of fetal biophysical profile and by itself it can predict pregnancy outcome. Very low values are associated with intrauterine growth restriction and renal anomalies of fetus, whereas high values may indicate fetal GI anomalies, maternal diabetes mellitus, and so forth. However, before deciding the cut-off standards for abnormal values for a local population, what constitutes a normal range for specific gestational age and the ideal interval of testing should be defined.Objectives. To establish reference standards for AFI for local population after 34 weeks of pregnancy and to decide an optimal scan interval for AFI estimation in third trimester in low risk antenatal women.Materials and Methods. A prospective estimation of AFI was done in 50 healthy pregnant women from 34 to 40 weeks at weekly intervals. The trend of amniotic fluid volume was studied with advancing gestational age. Only low risk singleton pregnancies with accurately established gestational age who were available for all weekly scan from 34 to 40 weeks were included in the study. Women with gestational or overt diabetes mellitus, hypertensive disorders of the pregnancy, prelabour rupture of membranes, and congenital anomalies in the foetus and those who delivered before 40 completed weeks were excluded from the study. For the purpose of AFI measurement, the uterine cavity was arbitrarily divided into four quadrants by a vertical and horizontal line running through umbilicus. Linear array transabdominal probe was used to measure the largest vertical pocket (in cm) in perpendicular plane to the abdominal skin in each quadrant. Amniotic fluid index was obtained by adding these four measurements. Statistical analysis was done using SPSS software (Version 16, Chicago, IL). Percentile curves (5th, 50th, and 95th centiles) were constructed for comparison with other studies. Cohen’sdcoefficient was used to examine the magnitude of change at different time intervals.Results. Starting from 34 weeks till 40 weeks, 50 ultrasound measurements were available at each gestational age. The mean (standard deviation) of AFI values (in cms) were 34 W: 14.59 (1.79), 35 W: 14.25 (1.57), 36 W: 13.17 (1.56), 37 W: 12.48 (1.52), 38 W: 12.2 (1.7), and 39 W: 11.37 (1.71). The 5th percentile cut-off was 8.7 cm at 40 weeks. There was a gradual decline of AFI values as the gestational age approached term. Significant drop in AFI was noted at two-week intervals. AFI curve generated from the study varied significantly when compared with already published data, both from India and abroad.Conclusion. Normative range for AFI values for late third trimester was established. Appreciable changes occurred in AFI values as gestation advanced by two weeks. Hence, it is recommended to follow up low risk antenatal women every two weeks after 34 weeks of pregnancy. The percentile curves of AFI obtained from the present study may be used to detect abnormalities of amniotic fluid for our population.

Introduction: Ovarian Cancer (OC) is one of the leading causes of cancer deaths among women. Despite recent advances in the medical field, such as surgery, chemotherapy, and radiotherapy interventions, there are only marginal improvements in the diagnosis of OC using clinical parameters, as the symptoms are very non-specific at the early stage. Owing to advances in computational algorithms, such as ensemble machine learning, it is now possible to identify complex patterns in clinical parameters. However, these complex patterns do not provide deeper insights into prediction and diagnosis. Explainable artificial intelligence (XAI) models, such as LIME and SHAP Kernels, can provide insights into the decision-making process of ensemble models, thus increasing their applicability.Methods: The main aim of this study is to design a computer-aided diagnostic system that accurately classifies and detects ovarian cancer. To achieve this objective, a three-stage ensemble model and a game-theoretic approach based on SHAP values were built to evaluate and visualize the results, thus analyzing the important features responsible for prediction.Results and Discussion: The results demonstrate the efficacy of the proposed model with an accuracy of 98.66%. The proposed model’s consistency and advantages are compared with single classifiers. The SHAP values of the proposed model are validated using conventional statistical methods such as the p-test and Cohen’s d-test to highlight the efficacy of the proposed method. To further validate the ranking of the features, we compared the p-values and Cohen’s d-values of the top five and bottom five features. The study proposed and validated an AI-based method for the detection, diagnosis, and prognosis of OC using multi-modal real-life data, which mimics the move of a clinician approach with a demonstration of high performance. The proposed strategy can lead to reliable, accurate, and consistent AI solutions for the detection and management of OC with higher patient experience and outcomes at low cost, low morbidity, and low mortality. This can be beneficial for millions of women living in resource-constrained and challenging economies.

Objective: The study was conducted to find the utility of three dimensional (3-D) ultrasound and Doppler sonography in differentiating benign and malignant endometrial lesions and to ascertain the association of sonology parameters with type, grade and stage of endometrial cancer. Materials and methods Women attending the gynaecology department of a tertiary care hospital, with a provisional diagnosis of carcinoma endometrium were subjected to three dimensional power Doppler ultrasound evaluation and assessment of vascular patterns. VOCAL (Virtual Organ Computer-aided Analysis) software was used to assess volume, Vascularisation Index (VI), Flow Index (FI) and Vascularisation Flow Index (VFI). Ultrasound parameters were compared with histologic diagnosis to evaluate the diagnostic performance using Receiver Operating Characteristic (ROC) Curve. Results: Sixty-four women were included in the study, 33 with benign and 31 with malignant endometrial lesions. Larger endometrial volume and higher Doppler indices correlated with malignant lesions. The variables with good discriminatory potential between benign and malignant status were VI and VFI, having a sensitivity of 90.3% and specificity of around 80%. VFI (adjusted odds ratio of 40.4; (95% CI – 8.46–192.88), p value < 0.001) was the only significant variable identified by multivariate logistic regression, when adjusted for age and post-menopausal status. Multiple global and focal vessel pattern was seen predominantly in malignant cases (specificity 93.9%), although the sensitivity was low (61.2%). Higher stages and grades of tumour and non-endometrioid types had higher Doppler indices, and requires further evaluation. Conclusions: 3-D ultrasound has good discrimination potential between benign and malignant endometrial lesions and could be useful as a screening tool. However, utility of 3-D tool for differentiation between tumour characteristics needs further validation.

Stillbirths, the tragic loss of a baby before or during delivery, presents a profound global health concern. Investigating the diverse causes and risk factors is essential to develop targeted interventions, enhance perinatal care, and reduce the incidence of this devastating outcome. The aim of this study was to identify the causes and possible risk factors of stillbirths in India. The study design is a protocol for systematic review. This systematic review will include all types of observational studies, such a cross-sectional surveys, descriptive studies, cohort studies and case-control studies from India. Studies will be retrieved from global databases like CINAHL, Medline through PubMed, EMBASE, SCOPUS, Web of Science, ProQuest Medical Library, Microsoft Academia, DOAJ, and Indian databases like Indian Citation Index, J-Gate, and Neonatal Database using database-specific keywords. Additionally, online hand searching will be done on the websites of the relevant Indian institutions. Primary studies' reference lists will be scrutinized for additional references if required. Two review authors (SN & AR) will independently screen, extract data, and critically appraise included studies. The Joanna Briggs Institute Critical Appraisal Checklist will be used to identify the quality and risk of bias of included studies. Any disagreements will be resolved in consultation with a third reviewer (BSN). Results will be reported as per the PRISMA guidelines. This systematic review will. identify and highlight the causes and potential risk factors of stillbirths in India, which will guide the healthcare team in planning and developing a standard protocol and policies to promote successful antenatal care, implement effective early interventions, and improve access to quality healthcare to reduce stillbirth rates in institutional, rural, and underserved areas as per the Indian healthcare system. These findings on causes and risk factors for stillbirth in India address a crucial gap in th literature and allow us to prepare a foundation for targeted interventions and policy recommendations in the healthcare system trajectory. The Protocol is registered in the International Prospective Register of Systematic reviews PROSPERO (CRD - 42024506186).