Pregnancy

Suppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model.

BACKGROUND AND OBJECTIVES: In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultr...

Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning.

A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R and SMAPE reaching 0.99 and 2.06%, respectively. The impact of design parameters on predicting kinetic constants for ultrasound degradation of trace pollutants was assessed using Shapley additi...

Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images.

BACKGROUND: Ultrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASL...

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms i...

A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

OBJECTIVE: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on ...

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that ...

Automated classification of multiple ophthalmic diseases using ultrasound images by deep learning.

BACKGROUND: Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ...

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for...

Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors.

PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant ...

Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dra...

Focused ultrasound enables selective actuation and Newton-level force output of untethered soft robots.

Untethered miniature soft robots have significant application potentials in biomedical and industrial fields due to their space accessibility and safe...

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.

OBJECTIVES: This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in...

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results.

Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also appli...

Prediction of post-delivery hemoglobin levels with machine learning algorithms.

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of proph...

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

OBJECTIVE: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the...

Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valu...

Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often ...

Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?

The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high qual...

Harnessing Disparities in Magnetic Microswarms: From Construction to Collaborative Tasks.

Individual differences in size, experience, and task specialization in natural swarms often result in heterogeneity and hierarchy, facilitating effici...

Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature.

BACKGROUND: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous ...