Pregnancy

Prediction of clinical pregnancy outcome after single fresh blastocyst transfer during in vitro fertilization: an ensemble learning perspective.

To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 patients (492 in training set and 123 in test set) who underwent the first single and fresh blastocyst transfer in the first IVF or intracytoplasmic sperm injection cycle performed in fertility centre of Shenzhen Zhongshan Obstetrics & Gynecology Hos...

Miniature Robots for Battling Bacterial Infection.

Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. However, bacterial infections usually form biofilms inside the body by aggregation and adhesion, preventing antibiotic penetration and increasing the likelihood of recurrence. Moreover, a substantial portion of the infection happens in those hard-to-access regions, making delivery of antibiotics to infect...

Leveraging machine learning to streamline the development of liposomal drug delivery systems.

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid ...

Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to variou...

Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy.

BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy n...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective...

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance aut...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Na...

An energy-aware protocol in wireless sensor networks using the scattered search algorithm and fuzzy logic.

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection d...

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.

BACKGROUND: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform th...

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include muci...

Assessment of pencil beam scanning proton therapy beam delivery accuracy through machine learning and log file analysis.

PURPOSE: Comprehensive Quality Assurance (QA) protocols are necessary for complex beam delivery systems like Pencil Beam Scanning (PBS) proton therapy...

ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widel...

Machine Learning Differentiates Between Benign and Malignant Parotid Tumors With Contrast-Enhanced Ultrasound Features.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is frequently used to distinguish benign parotid tumors (BPTs) from malignant parotid tumors (MPTs). I...

Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This stud...

Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.

BACKGROUND: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnosti...

Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape.

BACKGROUND: The WHO's recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perina...

Large language models to facilitate pregnancy prediction after in vitro fertilization.

We evaluated the efficacy of large language models (LLMs), specifically, generative pre-trained transformer-4 (GPT-4), in predicting pregnancy followi...

Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.

There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapi...

Fetal Face: Enhancing 3D Ultrasound Imaging by Postprocessing With AI Applications: Myth, Reality, or Legal Concerns?

The use of artificial intelligence (AI) platforms is revolutionizing the performance in managing metadata and big data. Medicine is another field wher...