Pregnancy

Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.

To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classified as dense breast were enrolled in this prospective study. Residual Network (ResNet) 101 classification model and ResNet with fully Convolutional N...

FreqYOLO: A uterine disease detection network based on local and global frequency feature learning.

Leiomyomas (LM) and adenomyosis (AM) are common gynecological diseases with high incidence rates and an increasing trend of affecting younger women. Accurate detection and differentiation of LM and AM in ultrasound images are crucial for selecting appropriate treatment options. Due to the heterogeneity of these two diseases, the location, size, and number of lesions often vary significantly, posin...

Robot-assisted ultrasound probe calibration for image-guided interventions.

BACKGROUND: Trackable ultrasound probes facilitate ultrasound-guided procedures, allowing real-time fusion of augmented ultrasound images and live vid...

Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.

BACKGROUND: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving o...

A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.

Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability di...

FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.

PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although ...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth re...

Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.

PURPOSE: Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted mo...

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.

Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal de...

Peptide-functionalized nanoparticles for brain-targeted therapeutics.

Despite the rapid development of nanoparticle (NP)-based drug delivery systems, intravenous delivery of drugs to the brain remains a major challenge d...

Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence.

BACKGROUND: Noninvasive volumetric measurements during the first trimester of pregnancy provide unique insight into human embryonic growth and develop...

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.

BACKGROUND: Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent....

Improving realism in abdominal ultrasound simulation combining a segmentation-guided loss and polar coordinates training.

BACKGROUND: Ultrasound (US) simulation helps train physicians and medical students in image acquisition and interpretation, enabling safe practice of ...

Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study.

BACKGROUND: Preoperative identification of genetic mutations is conducive to individualized treatment and management of papillary thyroid carcinoma (P...

Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurat...

Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.

We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique...

Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model.

OBJECTIVE: The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and...

Placing Objects on Table Is Preferred over Direct Handovers When Users Are Occupied.

Service robots commonly deliver objects through direct handovers, assuming users are fully attentive. However, in real-world scenarios, users are ofte...

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical...

Binding interaction of acetylcholinesterase with steroidal pregnanes: insight from machine learning and atomistic simulation.

Acetylcholinesterase (AChE) inhibition is a key strategy in the treatment of Alzheimer's disease and other neurodegenerative disorders. While pregnane...