Menopause

Developing a nomogram model for predicting non-obstructive azoospermia using machine learning techniques.

Azoospermia, defined by the absence of sperm in the ejaculate, manifests as obstructive azoospermia (OA) or non-obstructive azoospermia (NOA). Reliable predictive models utilizing biomarkers could aid in clinical decision-making. This study included 352 azoospermia patients, with 152 diagnosed with OA and 200 with NOA. The data were randomly divided into a training set (244 cases) and a validation...

Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients.

INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructive pulmonary disease (COPD) patients using chest computed tomography (CT) scans.

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.

Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persis...

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen ...

Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.

BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from ch...

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-energy X-ray absorptiometry (DXA) however, most patie...

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiom...

Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.

BACKGROUND: Osteoporosis (OP), often termed the "silent epidemic," poses a substantial public health burden. Emerging insights into the molecular func...

Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists' therapy management.

OBJECTIVES: Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims ...

Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.

Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to compl...

Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity.

This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective mod...

Comparison of Individualized Rescue Luteal Phase Support Strategies with Vaginal and Combined Vaginal & Subcutaneous Progesterone Administration in Artificial Frozen-Thawed Blastocyst Embryo Transfer Cycles Based on Serum Progesterone levels.

OBJECTIVES: Hormone replacement therapy (HRT) frozen embryo transfer (FET) cycles are common in assisted reproductive techniques. As the corpus luteum...

A recurrence model for non-puerperal mastitis patients based on machine learning.

OBJECTIVE: Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse aft...

A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.

UNLABELLED: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) imag...

Targeting protein-ligand neosurfaces with a generalizable deep learning tool.

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emer...

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteopo...

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.

Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteri...

Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception.

Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge th...

Beyond the hot flashes: how machine learning is uncovering the complexity of menopause-related depression.

BACKGROUND: The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, enc...

Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.

A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning ...