Revolutionizing Health and Technology
Join us in exploring new frontiers in innovation and discovery for Women's Health
About FETI
Our Mission
At FemEquity Tech Innovations LLC (FETI), we are dedicated to pioneering technological advancements to achieve gender equity, with a special focus on maternal health for Black, Indigenous, and Women of Color (BIWOC). Our dual mission is to support clinicians in fostering self-reflexivity and recognizing and addressing unrecognized biases contributing to maternal health disparities. We also support the empowerment of diverse women through innovative solutions, giving them a voice to drive positive health changes throughout pregnancy, birth, and beyond.
Maternal Health Conditions Enhanced through AI Technology
Gestational Diabetes (GDM)
Gestational diabetes is a condition characterized by high blood sugar levels that develop during pregnancy. The review identifies multiple AI models that predict the risk of GDM by analyzing factors such as maternal age, body mass index (BMI), and family history of diabetes. These models help in the early diagnosis and management of GDM, thereby reducing potential complications for the mother and child (Oprescu et al., 2020).
Hypertensive Disorders
Apart from pre-eclampsia, other hypertensive disorders during pregnancy, such as gestational hypertension, pose significant risks. AI applications focus on early detection, continuous blood pressure monitoring, and related physiological parameters. These technologies help manage hypertensive disorders effectively, reducing the risk of severe complications (Oprescu et al., 2020).
Preterm Birth
Preterm birth, defined as birth before 37 weeks of gestation, is a leading cause of neonatal mortality and morbidity. AI has been used to predict preterm birth by analyzing various biomarkers, clinical data, and environmental factors. Studies indicate that machine learning algorithms can significantly improve prediction accuracy, allowing timely interventions to prevent preterm births (Oprescu et al., 2020).
Labor and Delivery
AI applications in labor and delivery include predicting the need for interventions such as cesarean sections and optimizing delivery timing. Machine learning models evaluate various factors, including maternal health, fetal condition, and labor progression, to support decision-making in the delivery room. These applications aim to improve mother and baby outcomes (Oprescu et al., 2020).
Mental Health
Mental health issues such as anxiety, depression, and stress during pregnancy are significant risk factors for adverse outcomes. AI and affective computing are used to monitor and assess the emotional well-being of pregnant women. These technologies help identify those at risk of mental health disorders, enabling timely psychological support and interventions to ensure better pregnancy outcomes (Oprescu et al., 2020).
Mortality
Maternal and neonatal mortality rates can be significantly impacted by timely and accurate predictions of complications using AI. Machine learning models analyze a wide range of clinical, demographic, and socioeconomic data to identify at-risk pregnancies. Early identification enables proactive measures to prevent adverse outcomes and reduce mortality rates (Oprescu et al., 2020).
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