Pregnancy

Artificial Intelligence-Assisted Standard Plane Detection in Hip Ultrasound for Developmental Dysplasia of the Hip: A Novel Real-Time Deep Learning Approach.

Developmental dysplasia of the hip (DDH) includes a range of conditions caused by inadequate hip joint development. Early diagnosis is essential to prevent long-term complications. Ultrasound, particularly the Graf method, is commonly used for DDH screening, but its interpretation is highly operator-dependent and lacks standardization, especially in identifying the correct standard plane. This var...

Association between prenatal exposure to per- and polyfluoroalkyl substances and blood pressure among preschool-aged children: The moderating effect of child-age and the mediating effect of inflammatory cytokine.

Our aim is to evaluate the association of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) with offspring blood pressure (BP); examine the moderating effect of children's age; and the mediating effects of inflammatory cytokines. Data of 215 mother-child pairs were collected from the Guangxi Zhuang birth cohort. Levels of nine PFAS were measured during pregnancy, and offspring BP was...

Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.

BACKGROUND: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further compl...

Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fin...

Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric MRI and Deep Learning Integration.

Multiparametric MRI (mpMRI) has become an essential tool in the detection of prostate cancer (PCa) and can help many men avoid unnecessary biopsies. H...

Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention.

Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strateg...

Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes.

Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper pro...

Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammogr...

AI-enabled obstetric point-of-care ultrasound as an emerging technology in low- and middle-income countries: provider and health system perspectives.

BACKGROUND: In many low- and middle-income countries (LMICs), widespread access to obstetric ultrasound is challenged by lack of trained providers, wo...

De-speckling of medical ultrasound image using metric-optimized knowledge distillation.

Ultrasound imaging provides real-time views of internal organs, which are essential for accurate diagnosis and treatment. However, speckle noise, caus...

Development and validation of a deep learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis a multicenter study.

BACKGROUND: To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis ...

SPACE: Subregion Perfusion Analysis for Comprehensive Evaluation of Breast Tumor Using Contrast-Enhanced Ultrasound-A Retrospective and Prospective Multicenter Cohort Study.

OBJECTIVE: To develop a dynamic contrast-enhanced ultrasound (CEUS)-based method for segmenting tumor perfusion subregions, quantifying tumor heteroge...

Deep learning-enhanced development of innovative antioxidant liposomal drug delivery systems from natural herbs.

Free radical-mediated oxidative damage to biological macromolecules, such as DNA and proteins, significantly contributes to cellular ageing. Antioxida...

Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block ...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Listening deeper: neural networks unravel acoustic features in preterm infant crying.

Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. Howe...

Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review.

OBJECTIVE: To assess the methodological quality and the risk of bias, of studies that developed prediction models using Machine Learning (ML) techniqu...

Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.

BACKGROUND: Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging o...

Quality assessment of large language models' output in maternal health.

Optimising healthcare is linked to broadening access to health literacy in Low- and Middle-Income Countries. The safe and responsible deployment of La...

Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application.

A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug ...