Pregnancy

Optimizing a hybrid process of electrolysis ultrasound and persulfate for remediation of petroleum contaminated soils using AI models.

Research uses both RANSAC Regressor and Monte Carlo Optimization to improve the performance of electrolysis/ultrasound/persulfate system which detoxifies petroleum-contaminated soils. The Artificial Intelligence (AI) models used to optimize six process parameters showed X2 (humidity) and X3 (voltage) and X5 (surfactant) enhanced removal efficiency the most but X1 (pH) presented a robust negative i...

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal tests, the model outperformed both Junior and Senior sonographers. External tests showed the model's effectiv...

Iron metabolism and preeclampsia: new insights from bioinformatics analysis.

OBJECTIVE: Preeclampsia (PE) is a multifactorial systemic pregnancy disease, in which iron metabolism and ferroptosis play significant roles during it...

Assessment of readability, reliability, and quality of large language models in addressing frequently asked questions regarding prenatal screening for fetal chromosomal anomalies.

OBJECTIVE: To evaluate the responses of large language models (LLMs) to prenatal screening questions for fetal chromosomal anomalies in terms of scien...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries ...

Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.

This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicul...

Predictive value of subacromial motion metrics for the effectiveness of ultrasound-guided dual-target injection: a longitudinal follow-up cohort trial.

OBJECTIVE: Subacromial impingement syndrome (SIS) frequently causes shoulder pain. This study aimed to (1) assess the predictive utility of quantitati...

Knowledge mapping of ultrasound technology and triple-negative breast cancer: a visual and bibliometric analysis.

OBJECTIVE: This study aims to explore the application of ultrasound technology in triple-negative breast cancer (TNBC) using bibliometric methods. It ...

An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.

Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormaliti...

A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.

Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; however, the speckles, acoustic shadows, and signal a...

Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.

Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining v...

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen an...

Comparative analysis of ChatGPT 3.5 and ChatGPT 4 obstetric and gynecological knowledge.

Generative Pretrained Transformer (GPT) is one of the most ubiquitous large language models (LLMs), employing artificial intelligence (AI) to generate...

Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features and clinical parameters.

BACKGROUND: Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of cen...

Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.

BACKGROUND: Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation...

Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

BACKGROUND: Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed ...

Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation.

Fetal ultrasound (US) image segmentation plays an important role in fetal development assessment, maternal pregnancy management, and intrauterine surg...

Ultrasound Radio Frequency Time Series for Tissue Typing: Experiments on In-Vivo Breast Samples Using Texture-Optimized Features and Multi-Origin Method of Classification (MOMC).

OBJECTIVES: One of the most promising auxiliaries for screening breast cancer (BC) is ultrasound (US) radio-frequency (RF) time series. It has the sup...

Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.

This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step a...

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.

Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advanc...