Ӧ Tunc alp, JP Pena-Rosas, T Lawrie, M Bucagu, OT Oladapo, A Portela, A Metin G€ ulmezoglu a Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland b Department of Nutrition for Health and Development, World Health Organization, Geneva, Switzerland c Department of Maternal, Newborn, Child and Adolescent Health, World Health Organization, Geneva, Switzerland Correspondence: Dr Ӧ Tunc alp, Scientist, Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Avenue Appia 20, Geneva, Switzerland. Email tuncalpo@who.int
Shayan Abrar, Samura Rahman, Ishrat Jahan Momo
et al.
Polycystic Ovary Syndrome (PCOS) is a widespread disorder in women of reproductive age, characterized by a hormonal imbalance, irregular periods, and multiple ovarian cysts. Infertility, metabolic syndrome, and cardiovascular risks are long-term complications that make early detection essential. In this paper, we design a powerful framework based on transfer learning utilizing DenseNet201 and ResNet50 for classifying ovarian ultrasound images. The model was trained on an online dataset containing 3856 ultrasound images of cyst-infected and non-infected patients. Each ultrasound frame was resized to 224x224 pixels and encoded with precise pathological indicators. The MixUp and CutMix augmentation strategies were used to improve generalization, yielding a peak validation accuracy of 99.80% by Densenet201 and a validation loss of 0.617 with alpha values of 0.25 and 0.4, respectively. We evaluated the model's interpretability using leading Explainable AI (XAI) approaches such as SHAP, Grad-CAM, and LIME, reasoning with and presenting explicit visual reasons for the model's behaviors, therefore increasing the model's transparency. This study proposes an automated system for medical picture diagnosis that may be used effectively and confidently in clinical practice.
Medha Sharma, Supriya Khadka, Udit Chandra Aryal
et al.
As Large Language Models (LLMs) become integrated into daily life, they are increasingly used for personal queries, including Sexual and Reproductive Health (SRH), allowing users to chat anonymously without fear of judgment. However, current evaluation methods primarily focus on accuracy, often for objective queries in high-resource languages, and lack criteria to assess usability and safety, especially for low-resource languages and culturally sensitive domains like SRH. This paper introduces LLM Evaluation Framework (LEAF), that conducts assessments across multiple criteria: accuracy, language, usability gaps (including relevance, adequacy, and cultural appropriateness), and safety gaps (safety, sensitivity, and confidentiality). Using the LEAF framework, we assessed 14K SRH queries in Nepali from over 9K users. Responses were manually annotated by SRH experts according to the framework. Results revealed that only 35.1% of the responses were "proper", meaning they were accurate, adequate and had no major usability or safety related gaps. Insights include differences in performance between ChatGPT versions, such as similar accuracy but varying usability and safety aspects. This evaluation highlights significant limitations of current LLMs and underscores the need for improvement. The LEAF Framework is adaptable across domains and languages, particularly where usability and safety are critical, offering a pathway to better address sensitive topics.
The rapid adoption of generative AI (GenAI) chatbots has reshaped access to sexual and reproductive health (SRH) information, particularly following the overturning of Roe v. Wade, as individuals assigned female at birth increasingly turn to online sources. However, existing research remains largely model-centered, paying limited attention to user privacy and safety. We conducted semi-structured interviews with 18 U.S.-based participants from both restrictive and non-restrictive states who had used GenAI chatbots to seek SRH information. Adoption was influenced by perceived utility, usability, credibility, accessibility, and anthropomorphism, and many participants disclosed sensitive personal SRH details. Participants identified multiple privacy risks, including excessive data collection, government surveillance, profiling, model training, and data commodification. While most participants accepted these risks in exchange for perceived utility, abortion-related queries elicited heightened safety concerns. Few participants employed protective strategies beyond minimizing disclosures or deleting data. Based on these findings, we offer design and policy recommendations, such as health-specific features and stronger moderation practices, to enhance privacy and safety in GenAI-supported SRH information seeking.
Jason A. Galvis, Taylor B. Parker, Cesar A. Corzo
et al.
Vehicle movements, including vehicle cabs and trailers, play a role in disseminating disease in swine production. However, there are many information gaps about vehicle movements patterns that increase the probability of disease transmission, which is crucial in developing better preventive strategies. In this study we described the movement pattern of vehicle cabs and trailers and identified risk factors for porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED) farm's infectious status. We collected global positioning system (GPS) movement data from vehicle cabs and trailers for 18 months and basic information for 6621 farms in the U.S. For the vehicle movement data, we estimated 66 variables and evaluate their association with farms PRRS and PED status. Our univariate analysis showed that 56 variables were significant associated (p < 0.05) to PED and PRRS farm status. Within these variables, vehicle visit frequency and previous exposition to positive farms were the main risk factors for both diseases. Otherwise, increased vehicle cab and trailer loyalty for farm shipments and vehicle cleaning and disinfection events were protective factors. In the multivariate model, each additional weekly visit by a vehicle cab that had been exposed to a positive farm one day before the shipment was associated with a 234\% and 243\% increase in the odds of a farm testing PRRS- and PED-positive, respectively. Our analysis revealed that vehicle contact history play a crucial role in the transmission of PRRS and PED. These findings can provide insights to develop more target strategies aimed at reducing the transmission and outbreaks linked to vehicle movements in swine production.
Abstract Objectives To investigate the relationship between the prevalence of circadian syndrome (CircS) and infertility. Methods A cross-sectional study was conducted involving 14,948 participants from the National Health and Nutrition Examination Survey (NHANES) (2013–2018). Information on infertility, CircS, age, education level, marital status, age at menarche, use of female hormones, poverty-income ratio (PIR), body mass index (BMI), drinking status, smoking status, use of birth control pills, pelvic infections, and race was collected from all participants. Logistic regression was used to explore the relationship between CircS prevalence and infertility. Interaction and stratified analyses were conducted according to age, PIR, age at menarche, drinking status, and smoking status. Results In total, 2282 participants were included, and the prevalence of infertility was 16.4% (374/2282). The prevalence of infertility was also significantly higher in the CircS group than in the non-CircS group ( P < 0.001). Multivariable logistic regression in the fully adjusted model showed that the prevalence of infertility was considerably higher in those with CircS than in those without CircS (odds ratios = 2.88, 95% confidence interval 2.16–3.86). In addition, the results of subgroup and stratified analyses were robust. Conclusions Our findings indicate that CircS is significantly associated with the likelihood of infertility, independent of confounding factors. These results highlight the importance of further investigation into the mechanisms underlying this association.