Pregnancy

Leveraging Large Language Models to Develop an Interpretable Prediction Model for Postpartum Hemorrhage Prior to the Onset of Labor

To evaluate whether large language models (LLMs) applied to prenatal clinical notes can predict postpartum hemorrhage (PPH) prior to the onset of labor and to compare model performance across outcome definitions, including a novel intervention-based definition. We conducted a retrospective cohort study of 19,992 deliveries within a large regional health network. Two outcome definitions for PPH wer...

OphthUS-GPT: Multimodal AI for Automated Reporting in Ophthalmic B-Scan Ultrasound

The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-limited settings. The shortage of ophthalmologists and lack of standardized reporting creates an urgent need for AI systems capable of automated reporting and interactive decision support. To develop OphthUS-GPT, a multimodal AI system integrating BLIP and DeepSeek models for automated report generatio...

Genetic Architecture and Risk Prediction of Gestational Diabetes Mellitus in over 116,144 Chinese Pregnancies

Gestational diabetes mellitus (GDM), a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding it...

AI-Driven Fluorescence Peak Analysis for Chromosomal Aneuploidy Detection: A Python-Based Machine Learning Approach for Enhanced Accuracy and Efficiency

Chromosomal aneuploidy, a condition characterized by an abnormal number of chromosomes, is a major genetic disorder affecting human reproduction, lead...

Benchmarking pathology foundation models for non-neoplastic pathology in the placenta

Machine learning (ML) applications within diagnostic histopathology have been extremely successful. While many successful models have been built using...

Linking physical food outlets to online platforms: A cross-sectional machine learning approach to analysing socioeconomic variations in Great Britain

Physical food outlets are increasingly offering delivery through Online Food Delivery Service (OFDS) platforms, but the scale of this expansion remain...

Automated cervix biometry, volumetry and normative models for 3D motion-corrected T2-weighted 0.55-3T fetal MRI during 2nd and 3rd trimesters

Fetal MRI provides superior tissue contrast and true 3D spatial information however there is only a limited number of number of MRI studies investigat...

Limited Echocardiogram Acquisition by Clinicians Aided with Deep Learning: A Randomized Controlled Trial

Deep learning (DL) programs can aid in the acquisition of echocardiograms by medical professionals not previously trained in sonography, potentially a...

Association of Deep Learning-Derived Histologic Features of Placental Chorionic Villi with Maternal and Infant Characteristics in the New Hampshire Birth Cohort Study

Quantification of placental histopathological structures is challenging due to a limited number of perinatal pathologists, constrained resources, and ...

AI vs. Traditional ultrasound study in Congenital Heart Defect Detection: A Systematic review

Prenatal detection rates for CHD have increased with improved ultrasound technology and imaging, the use of the first trimester fetal echocardiography...

Hierarchical representation learning of preeclampsia interactome connecting endometrial maturation, placentation, chorioamnionitis, and HELLP syndrome

Existing proposed pathogenesis for preeclampsia (PE) was only applied for early-onset PE (EOPE). Our previous work identified the transcriptome to dec...

Adherence Risk Stratification in Physiatry: A Multivariate Analysis of Factors in Community-Based Care Using Algorithmic Modeling Techniques

Missed appointments represent a double-edged sword in community health settings. Policies designed to retain patients and ensure continuity of care fo...

Generative AI for Thematic Analysis in a Maternal Health Study: Coding Semi-structured Interviews using Large Language Models (LLMs)

The coding of semi-structured interview transcripts is a critical step for thematic analysis of qualitative data. However, the coding process is often...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

Evaluation of Machine Learning Models for Early Prediction of Gestational Diabetes Using Retrospective Electronic Health Records from Current and Previous Pregnancies

To assess the performance of machine learning (ML) models in predicting gestational diabetes mellitus (GDM) using electronic health record (EHR) data ...

Creation of an Open-Access Lung Ultrasound Image Database For Deep Learning and Neural Network Applications

Lung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray a...

Developing a GraphRAG-enabled local-LLM for Gestational Diabetes Mellitus

This paper re-imagines a world of abundance in the treatment of chronic diseases such as Tpe 2 Diabetes. It asks: what if preventive and diagnostic re...

Development and validation of diagnostic and prognostic prediction tools for dental caries in young children: A protocol

Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired qualit...

The allostatic overload in pregnancy during the COVID-19 pandemic and potential effects on the health of the mother-child dyad: Study Protocol

Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...