Latest AI and machine learning research in pregnancy for healthcare professionals.
This study introduces a motion-based learning network with a global-local self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often limited by a narrow field of view and the inability to effectively visualize complex 3D structures. The 3D freehand technique, which aligns sequential 2D images for 3D recon...
Ultrasound (US) imaging is clinically invaluable due to its noninvasive and safe nature. However, interpreting US images is challenging, requires significant expertise, and time, and is often prone to errors. Deep learning offers assistive solutions such as segmentation. Supervised methods rely on large, high-quality, and consistently labeled datasets, which are challenging to curate. Moreover, ...
Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reve...
Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic mani...
OBJECTIVE: To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).
OBJECTIVE: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriatene...
BACKGROUND: Infant mortality remains a critical public health issue, reflecting the overall health and well-being of a population. Accurate prediction...
BACKGROUND: This study aimed to evaluate the effects of an oral nutritional supplement (ONS) enriched with hydroxy-methyl-butyrate (HMB) in subjects w...
Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI)...
Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datas...
Purpose: This study examines the core traits of image-to-image translation (I2I) networks, focusing on their effectiveness and adaptability in every...
We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss fee...
The Segment Anything Model (SAM) has set a new standard in interactive image segmentation, offering robust performance across various tasks. However...
Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the ...
Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D ...
Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ...
Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often fac...
With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive na...
In several two-sided markets, including labor and dating, agents typically have limited information about their preferences prior to mutual interact...
Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prost...