Pregnancy

EndoExtract: Co-Designing Structured Text Extraction from Endometriosis Ultrasound Reports

Endometriosis ultrasound reports are often unstructured free-text documents that require manual abstraction for downstream tasks such as analytics, machine learning model training, and clinical auditing. We present \textbf{EndoExtract}, an on-premise LLM-powered system that extracts structured data from these reports and surfaces interpretive fields for human review. Through contextual inquiry wit...

Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound

Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SSL) is commonly used for fetal US image analysis, existing SSL methods typically rely on random limited selection, which can lead to suboptimal model performance by overfitting to homogeneous labeled data. To address this...

Development of a prediction model for infant hospitalization and death using clinical features assessed by community health workers during routine postnatal home visits in Dhaka, Bangladesh

Introduction: To improve upon the World Health Organization (WHO) 8 danger signs used to identify young infants (<2 months) requiring referral during ...

FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation

Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), y...

Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)

This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep lear...

Vision Models for Medical Imaging: A Hybrid Approach for PCOS Detection from Ultrasound Scans

Polycystic Ovary Syndrome (PCOS) is the most familiar endocrine illness in women of reproductive age. Many Bangladeshi women suffer from PCOS disease ...

COLLEMBOT: AI-Based Counting of Collembola for OECD 232 Tests

Ecotoxicological tests with soil organisms, such as the collembolan Folsomia candida, are essential for assessing chemical risks in terrestrial ecosys...

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training

The functions of different regions of the human brain are closely linked to their distinct cytoarchitecture, which is defined by the spatial arrangeme...

KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure

Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identif...

Onco-Seg: Adapting Promptable Concept Segmentation for Multi-Modal Medical Imaging

Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We...

OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics

Deep visual proteomics (DVP) is an emerging approach for cell type-specific and spatially resolved proteomics. However, its broad adoption has been co...

Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data. White light (...

Artificial intelligence predicts pregnancy complications based on cytokine profiles.

BACKGROUND: Early prediction of pregnancy complications is important for adequate and timely prevention, management, and reducing maternal/fetal patho...

Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images.

PURPOSE: We propose and evaluate multimodal deep learning (DL) approaches that combine ultrasound (US) B-mode and Nakagami parametric images for breas...

Machine learning approaches for the prediction of retained placenta in dairy cows.

Retained placenta (RP) is a reproductive disorder that causes significant financial losses to the dairy industry. Predicting RP risk in cows post-calv...

Intelligent transformation of ultrasound-assisted novel solvent extraction plant active ingredients: Tools for machine learning and deep learning.

Ultrasound-assisted novel solvent extraction enhances plant bioactive compound yield via cavitation, mechanical, and thermal mechanisms. However, the ...

UltrasOM: A mamba-based network for 3D freehand ultrasound reconstruction using optical flow.

BACKGROUND: Three-dimensional (3D) ultrasound (US) reconstruction is of significant value in clinical diagnosis, characterized by its safety, portabil...

A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal funct...

Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation

The development of robust deep learning models for breast ultrasound (BUS) image analysis is significantly constrained by the scarcity of expert-ann...

Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation

Ultrasound imaging is a prevalent diagnostic tool known for its simplicity and non-invasiveness. However, its inherent characteristics often introdu...