Obstetrics & Gynecology

Pregnancy

Latest AI and machine learning research in pregnancy for healthcare professionals.

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A dual-decoder banded convolutional attention network for bone segmentation in ultrasound images.

BACKGROUND: Ultrasound (US) has great potential for application in computer-assisted orthopedic surg...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, gi...

Deep-DM: Deep-Driven Deformable Model for 3D Image Segmentation Using Limited Data.

Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis,...

Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contri...

A multimodal machine learning model for the stratification of breast cancer risk.

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer r...

Training humans to supplement a machine learning system: The role of guides in a digital mental health intervention.

Machine learning (ML) is increasingly prevalent in mental health care, with contemporary initiatives...

Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessi...

Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15-24 years in East Africa.

Pregnancy termination is still a sensitive and continuing public health issue due to several politic...

An explainable ultrasound-based machine learning model for predicting reproductive outcomes after frozen embryo transfer.

RESEARCH QUESTION: Can an optimal machine learning model be developed to predict reproductive outcom...

DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor.

Cardiotocography (CTG) is the main tool available to detect neonatal acidemia during delivery. Prese...

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning.

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If...

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary ...

Active Inference and Deep Generative Modeling for Cognitive Ultrasound.

Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. D...

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sou...

Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography.

The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depe...

Automatic Segmentation of Abdominal Aortic Aneurysms From Time-Resolved 3-D Ultrasound Images Using Deep Learning.

Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical pr...

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing.

Autonomous ultrasound image quality assessment (US-IQA) is a promising tool to aid the interpretatio...

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