Pregnancy

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision) advance evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes(formulating clinical questions, conducting literat...

Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.

To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients compared with conventional ultrasound diagnosis. In this prospective study (ChineseClinicalTrials.gov identifier, ChiCTR2200057253), 32 radioactive patients were examined by conventional ultrasound and MGIUS-R3 robot-assisted ultrasound from March 2022 to June 2022. After two scans, patients complete...

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.

Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This ...

Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.

OBJECTIVE: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the availabl...

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlan...

A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.

Obstetric ultrasound (OBUS) is recommended as part of antenatal care for pregnant individuals worldwide. To better understand current uses of OBUS in ...

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely empl...

Construction and evaluation of machine learning-based predictive models for early-onset preeclampsia.

OBJECTIVE: To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using mac...

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.

BACKGROUND: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertain...

Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.

BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associa...

Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.

This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by F...

Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.

OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug...

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image.

Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantificati...

Unveiling genetic basis of seedling emergence from deep soil depth under dry direct- seeded conditions in rice ( L.).

Water scarcity and labor shortage pose significant challenges in rice farming. Direct-seeded rice (DSR) is an efficient method that conserves water, r...

Automatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.

With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D...

ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.

BACKGROUND AND OBJECTIVE: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically sign...

K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data.

AIMS: The objective of this study is to illustrate the application of a machine learning algorithm, K Nearest Neighbor () to impute missing alcohol da...

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict panc...

A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

OBJECTIVE: This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with h...

Pregnancy-Induced Cardiomyopathy: What Case Managers Need to Know.

A new form of stethoscope with artificial intelligence (AI) capabilities may make the difference between early detection of pregnancy-induced cardiomy...