Pregnancy

Ambiguous and Incomplete: Natural Language Processing Reveals Problematic Reporting Styles in Thyroid Ultrasound Reports.

OBJECTIVE: Natural language processing (NLP) systems convert unstructured text into analyzable data. Here, we describe the performance measures of NLP to capture granular details on nodules from thyroid ultrasound (US) reports and reveal critical issues with reporting language.

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation.

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normal...

Real-Time Ultrasound Doppler Tracking and Autonomous Navigation of a Miniature Helical Robot for Accelerating Thrombolysis in Dynamic Blood Flow.

Untethered small-scale robots offer great promise for medical applications in complex biological environments. However, challenges remain in the contr...

Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image.

Deep learning methods, especially convolutional neural networks, have advanced the breast lesion classification task using breast ultrasound (BUS) ima...

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh.

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Addit...

Advancing primary care with Artificial Intelligence and Machine Learning.

Primary care is the largest healthcare delivery platform in the US. Facing the Artificial Intelligence and Machine Learning technology (AI/ML) revolut...

A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images.

OBJECTIVE: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint ta...

The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.

Achieving The United Nations sustainable developments goals by 2030 will be a challenge. Researchers around the world are working toward this aim acro...

Weakly-supervised deep learning for ultrasound diagnosis of breast cancer.

Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We ...

Preconcentration and determination of trace Hg(ii) using ultrasound-assisted dispersive solid phase microextraction.

Defect rich molybdenum disulfide (MoS) nanosheets were hydrothermally synthesized and their potential for ultrasound assisted dispersive solid phase m...

TypeSeg: A type-aware encoder-decoder network for multi-type ultrasound images co-segmentation.

PURPOSE: As a portable and radiation-free imaging modality, ultrasound can be easily used to image various types of tissue structures. It is important...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by t...

Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could ...

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

OBJECTIVES: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors a...

Extracting experimental parameter entities from scientific articles.

Systematic reviews are labor-intensive processes to combine all knowledge about a given topic into a coherent summary. Despite the high labor investme...

MBANet: Multi-branch aware network for kidney ultrasound images segmentation.

Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To allevi...

Development and Validation of an Analytical Method to Quantitate Hydroxycitric Acid, the Key Constituent in Extract, in Rodent Plasma and Fetus.

extract (GCE) is a popular botanical supplement used in weight loss products. Hydroxycitric acid (HCA) is the principal component in GCE. Due to lack...

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation.

Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health car...

Validation of Analytical Method for Determination of Thallium in Rodent Plasma and Tissues by Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

Thallium (Tl) can be released as a byproduct of smelting, mining, and other industries, causing human exposure. There are knowledge gaps on the toxici...

An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregn...