Pregnancy

Risk-prediction models for postpartum depression: Assessment of risk of bias using PROBAST+AI.

OBJECTIVE: The postpartum depression (PPD) risk prediction model is an effective risk stratification tool and is expected to play a significant role in the early detection and intervention of PPD. This study aims to summarize the existing evidence on PPD risk prediction models and provide references for their development, validation, and clinical application. METHODS: We searched PubMed, EMBASE, a...

LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research.

Approximately 30-50% of Papillary thyroid carcinoma (PTC) patients develop cervical lymph nodes (LNs) metastasis, significantly increasing the risk of disease recurrence and impacting long-term outcomes. We introduced an open-access multicenter lymph node ultrasound image database (LymphUs) specifically designed to advance research in LN assessment for PTC. Ultrasound imaging was performed on PTC ...

An open B-mode ultrasound database for deep learning-based atherosclerotic plaque segmentation.

Cardiovascular events, predominantly ischemic, account for approximately 32% of global mortality and are expected to increase approximately 30% by 203...

Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.

The integration of artificial intelligence (AI) into healthcare is accelerating and maternity care is at a pivotal moment for the strategic implementa...

Data-Driven Phenotypic Clusters of Gestational Diabetes Mellitus and Associations With Risk of Perinatal Complications and Postpartum Diabetes.

OBJECTIVE: Management of gestational diabetes mellitus (GDM) largely follows a uniform approach, despite growing recognition of GDM heterogeneity. We ...

Value of Endobronchial Ultrasound-Derived Radiomics in Differentiating Pulmonary Sarcoidosis from Mediastinal Lymph Node Tuberculosis.

INTRODUCTION: To explore the feasibility of an ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS) for differentiatin...

SSI-Net: A hybrid physics-constrained deep learning framework for quantitative ultrasound speed-of-sound reconstruction.

OBJECTIVE: Quantitative ultrasound tomography faces challenges in reconstructing speed‑of‑sound (SoS) distributions due to the ill‑posed nature of the...

Ultrasonic-assisted extraction, purification, identification, and bioactivity evaluation of phlorotannins from Sargassum fusiforme: A comparative study of RSM and SVM-GA.

Sargassum fusiforme is a medicinal and edible species present in China, Korea, and Japan, and its phlorotannins are considered valuable bioactive comp...

Amniotic fluid metabolic biomarkers of fetal physiology and pregnancy success†.

Amniotic fluid (AF) profiling provides a minimally invasive window into early fetal physiology. We characterized the AF metabolome from first trimeste...

Explainable tabular deep learning models for antenatal cesarean delivery prediction in multiparous women.

LAMB1 regulates COL3A1 and RAC1 expression during subchorionic hemorrhage progression.

BACKGROUND: Subchorionic hemorrhage (SCH) is characterized by a fluid-filled hypoechoic area in early pregnancy. This study investigates how laminin s...

Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.

OBJECTIVES: To develop and validate a machine learning model integrating ultrasound radiomics and clinicopathological parameters to predict intrahepat...

Radiomics in fetal brain MRI: a narrative review.

Fetal MRI has emerged as a crucial supplement to prenatal ultrasonography in the evaluation of the developing brain and in identifying congenital defe...

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

A significant proportion (45%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, particularly prevalent in low-...

The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.

Integrating artificial intelligence (AI) into maternal and neonatal health (MNH) offers significant opportunities for enhancing patient care through a...

Ferroptosis-related gene BACH1 is associated with preeclampsia and shows potential as a biomarker.

A multimodal feature disentanglement model for lymphadenopathy diagnosis based on BUS and CDFI ultrasound videos: a retrospective, prospective, multicenter study.

OBJECTIVES: This study developed and validated a deep learning model for diagnosing lymphadenopathy (LA) using B-mode ultrasound (BUS) and color Doppl...

Artificial intelligence-enabled personalisation of oral drug delivery: From data-driven design to on-demand manufacturing.

Artificial intelligence (AI) is reshaping pharmaceutical research by enabling data-intensive tasks to be performed with unprecedented speed and accura...

Deep learning based gestational age estimation from multi-view fetal brain magnetic resonance imaging.

BACKGROUND: Gestational age (GA) is essential for assessing fetal development, but conventional methods such as last menstrual period and ultrasound a...

Infrared thermography in the detection of arthritis: a systematic review of diagnostic performance compared with joint ultrasound.

Early detection of arthritis in autoimmune rheumatic diseases (ARDs) is critical to prevent irreversible damage. Joint ultrasound (US) offers high sen...