Pregnancy

Understanding the importance of social and environmental stressors in predicting maternal sleep quality during late pregnancy.

Poor pregnancy sleep quality has been linked to adverse pregnancy and birth outcomes, particularly in late pregnancy when sleep symptoms worsen. We aimed to identify potential important predictors of late-pregnancy sleep quality from social and environmental stressors and pregnancy health conditions, using a machine-learning approach. In the MADRES cohort, 687 mothers reported late-pregnancy sleep...

A Hybrid Modelling Framework for the description of friction forces in autoinjector devices.

Autoinjectors (AJs) are medical devices enabling precise subcutaneous self-administration of medicines. An important challenge in modelling these devices is accurately predicting injection time, which significantly impacts device usability, drug delivery and patient experience. Existing literature models for injection time are based on the balance of forces acting on the stopper. These typically i...

Enhancing Tenosynovitis Diagnosis With XGBoost: A Machine Learning Approach Using Ultrasound Data.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic autoimmune disorder characterized by chronic inflammation and progressive joint destruction. Tenos...

Radiomic Carotid Plaque Features Integrated into Machine Learning Models for Cardiovascular Risk Prediction.

OBJECTIVE: Carotid plaque detected by ultrasound is associated with major adverse cardiovascular events (MACE) and can be characterized using manual o...

New Frontiers of Skin Ultrasound in Precision Medicine.

Ultrasound has emerged as a versatile, non-invasive imaging technique in dermatology, offering real-time, high-resolution visualization of cutaneous s...

Economic Impact of a Deep Learning Algorithm for Automated Head and Neck Surgery Referral Triage.

OBJECTIVE: To quantify the comprehensive financial impact from implementing a deep learning algorithm for assistive patient triage within a high-volum...

AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches.

BACKGROUND: Trisomy 21 (Down syndrome) remains the most prevalent autosomal aneuploidy, necessitating accurate prenatal diagnosis. While cell-free fet...

Protocol for the external validation of machine learning models for risk prediction in early-onset preeclampsia: a multicenter prospective cohort study.

BACKGROUND: Early-onset preeclampsia (eoPE), defined as preeclampsia occurring before 34 weeks of gestation, remains one of the leading causes of seve...

Smart feeding: the role of artificial intelligence and integrated nutrition platforms in the ICU.

PURPOSE OF REVIEW: Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electro...

FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.

BACKGROUND AND OBJECTIVE: Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per ...

Applying machine-learning models to successful vaginal birth after two cesareans.

BACKGROUND: Vaginal birth after two cesarean deliveries (VBAC2) is increasingly recognized as a reasonable and, in selected cases, preferable option. ...

Analysis of blastocyst morphokinetics assessed by time-lapse technology and its correlation with human IVF treatment outcome.

Human embryo selection in in vitro fertilisation (IVF) treatments is traditionally based on evaluating cell number, morphology, and fragmentation to s...

Expeditionary Point-of-Care Ultrasound for Combat and Austere Environments.

BACKGROUND: Point-of-care ultrasound (POCUS) enhances combat survivability, yet civilian standards often fail to address battlefield constraints. This...

A machine learning model integrating two-dimensional ultrasound radiomics and clinical parameters for differentiating membranous nephropathy from IgA nephropathy.

BACKGROUND: Membranous nephropathy (MN) and IgA nephropathy (IgAN) are the two most common primary glomerular diseases in China, with distinct pathoph...

Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.

Despite recent advances in medical image segmentation, deep learning applications in musculoskeletal ultrasound remain limited by small labeled datase...

Exploring artificial intelligence in point-of-care and standard breast ultrasound: a paired reader study.

BackgroundBreast cancer diagnoses are limited in low- and middle-income settings due to lack of medical resources. In these settings, point-of-care ul...

Diagnostic accuracy of an artificial intelligence-based breast ultrasound tool in pregnant and lactating patients.

OBJECTIVES: To assess the diagnostic performance of an artificial intelligence (AI)-based decision support tool for breast ultrasound in pregnant and ...

Quantification of Histotripsy Dosage Using Machine Learning-Enhanced Ultrasound Imaging Analysis: Correlation with Histological Outcomes.

RATIONALE AND OBJECTIVES: Histotripsy is a noninvasive ultrasound therapy that mechanically disrupts target tissue through controlled acoustic cavitat...

Hybrid vision transformer and ensemble machine learning framework for automated atherosclerotic plaque classification in intravascular ultrasound imaging.

Atherosclerotic plaque classification in intravascular ultrasound (IVUS) imaging is crucial for cardiovascular disease diagnosis and treatment plannin...

Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin.

Premature rupture of membranes (PROM) and preterm PROM (PPROM) are significant obstetric complications, affecting 10-20% and 3% of pregnancies globall...