Pregnancy

Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.

BACKGROUND: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it's difficult to make an accurate differential diagnosis. This study is intended to assess the performance of ...

Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7.

Target 3.7 of the Sustainable Development Goals (SDGs) aims for universal access to sexual and reproductive health (SRH) services by 2030, including family planning services, information, education, and integration into national strategies. In contemporary times, reproductive medicine is progressively incorporating artificial intelligence (AI) to enhance sperm cell prediction and selection, in vit...

AI and Technology Enabled Clinical Workflow Redesign.

The rapid evolution of digital health technologies has significantly transformed health care delivery, particularly in the realm of virtual care. This...

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.

BACKGROUND: Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to ...

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common fin...

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.

PURPOSE: This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WG...

Predicting preterm birth using electronic medical records from multiple prenatal visits.

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic ne...

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.

Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding prag...

Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.

Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep...

Interactively Fusing Global and Local Features for Benign and Malignant Classification of Breast Ultrasound Images.

OBJECTIVE: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. H...

Readability, quality and accuracy of generative artificial intelligence chatbots for commonly asked questions about labor epidurals: a comparison of ChatGPT and Bard.

INTRODUCTION: Over 90% of pregnant women and 76% expectant fathers search for pregnancy health information. We examined readability, accuracy and qual...

Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.

Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal he...

Enhancing decision-making in tubal ectopic pregnancy using a machine learning approach to expectant management: a clinical article.

OBJECTIVE: To refine decision-making regarding expectant management for ectopic pregnancy (EP) using machine learning.

Identification of pregnancy in women based on fingertip pulse using a multi-feature fusion neural network model.

This study proposes a rapid method for determining pregnancy status based on fingertip pulse signals. A finger pulse sensor collects data, which is pr...

Actuation-Mediated Compression of a Mechanoresponsive Hydrogel by Soft Robotics to Control Release of Therapeutic Proteins.

Therapeutic proteins, the fastest growing class of pharmaceuticals, are subject to rapid proteolytic degradation in vivo, rendering them inactive. Sop...

Predicting maternal risk level using machine learning models.

BACKGROUND: Maternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is par...

Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.

Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal u...

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.

BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers pr...

Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.

PURPOSE: This study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC).

Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.

MAIN OBJECTIVES: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal...