Pregnancy

Artificial intelligence in obstetric anaesthesiology - the future of patient care?

The use of artificial intelligence (AI) in obstetric anaesthesiology shows great potential in enhancing our practice and delivery of care. In this narrative review, we summarise the current applications of AI in four key areas of obstetric anaesthesiology (perioperative care, neuraxial procedures, labour analgesia and obstetric critical care), where AI has been employed to varying degrees for deci...

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems.

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intel...

Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease.

We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney dis...

Machine learning models of cerebral oxygenation (rcSO) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO) in detecting term infants with brain injury...

Ultrasound for breast cancer detection: A bibliometric analysis of global trends between 2004 and 2024.

With the advancement of computer technology and imaging equipment, ultrasound has emerged as a crucial tool in breast cancer diagnosis. To gain deeper...

Artificial intelligence-driven predictive framework for early detection of still birth.

Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive ...

A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting ...

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently ...

[AI-supported decision-making in obstetrics - a feasibility study on the medical accuracy and reliability of ChatGPT].

The aim of this study is to investigate the feasibility of artificial intelligence in the interpretation and application of medical guidelines to supp...

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmenta...

AI driven interpretable deep learning based fetal health classification.

In this study, a deep learning model is proposed for the classification of fetal health into 3 categories: Normal, suspect, and pathological. The prim...

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to s...

The utilization of artificial intelligence in enhancing 3D/4D ultrasound analysis of fetal facial profiles.

Artificial intelligence (AI) has emerged as a transformative technology in the field of healthcare, offering significant advancements in various medic...

The Use of Remote Presence Robotic Tele-Presentation in Rural and Remote Canada: A Systematic Review.

One potential solution to limited health care in rural and remote regions is remote presence robotic tele-presentation to allow health care providers...

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the mater...

Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data.

BACKGROUND: Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of norm...

Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting ...

Beamforming-integrated neural networks for ultrasound imaging.

Sparse matrix beamforming (SMB) is a computationally efficient reformulation of delay-and-sum (DAS) beamforming as a single sparse matrix multiplicati...

Bubble-Inspired Multifunctional Magnetic Microrobots for Integrated Multidimensional Targeted Biosensing.

Microrobots possessing multifunctional integration are desired for therapeutics and biomedicine applications. However, existing microrobots with desir...