Obstetrics & Gynecology

Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such as cervical vertebral maturation (CVM) staging have limitations due to observer variability and complexity. AI has the potential to automate CVM asse...

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19's Impacts in Pregnancy: Cross-Sectional Pilot Study.

BACKGROUND: The COVID-19 pandemic has significantly strained health care systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an "infodemic" of misinformation, particularly prevalent in women's health, has emerged. This challenge has been pivotal for health care providers, especially gynecologists and obstetricians, in managing pregna...

Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning.

To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT...

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personal...

Conventional and machine learning-based analysis of age, body weight and body height significance in knot position-related thyrohyoid and cervical spine fractures in suicidal hangings.

The thyrohyoid complex and cervical spine fracture distribution patterns may reflect the knot position as the force distribution by the noose to diffe...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical to...

Untargeted metabolomics and machine learning unveil the exposome and metabolism linked with the risk of early pregnancy loss.

Early pregnancy loss (EPL) may result from exposure to emerging contaminants (ECs), although the underlying mechanisms remain poorly understood. This ...

Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning.

BACKGROUND: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task whe...

Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.

OBJECTIVE: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the availabl...

Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.

Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. ...

Machine learning model for menstrual cycle phase classification and ovulation day detection based on sleeping heart rate under free-living conditions.

The accurate classification of menstrual cycle phases and detection of ovulation is critical for women's health management, particularly in addressing...

Unveiling genetic basis of seedling emergence from deep soil depth under dry direct- seeded conditions in rice ( L.).

Water scarcity and labor shortage pose significant challenges in rice farming. Direct-seeded rice (DSR) is an efficient method that conserves water, r...

Incidence and Predictors of Acute Kidney Injury Following Advanced Ovarian Cancer Cytoreduction at a Tertiary UK Centre: An Exploratory Analysis and Insights from Explainable Artificial Intelligence.

BACKGROUND/OBJECTIVES: The incidence of acute kidney injury (AKI) following advanced epithelial ovarian cancer (EOC) surgery has not been extensively ...

K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data.

AIMS: The objective of this study is to illustrate the application of a machine learning algorithm, K Nearest Neighbor () to impute missing alcohol da...

A robust and generalized framework in diabetes classification across heterogeneous environments.

Diabetes mellitus (DM) represents a major global health challenge, affecting a diverse range of demographic populations across all age groups. It has ...

OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.

BACKGROUND: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for earl...

An automatic cervical cell classification model based on improved DenseNet121.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to dete...

A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

OBJECTIVE: This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with h...

Pregnancy-Induced Cardiomyopathy: What Case Managers Need to Know.

A new form of stethoscope with artificial intelligence (AI) capabilities may make the difference between early detection of pregnancy-induced cardiomy...

Intra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development.

OBJECTIVES: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using h...