Endocrinology

Menopause

Latest AI and machine learning research in menopause for healthcare professionals.

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Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Recepto...

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.

Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce produ...

RAIN: machine learning-based identification for HIV-1 bNAbs.

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of...

A Machine Learning Framework for Screening Plasma Cell-Associated Feature Genes to Estimate Osteoporosis Risk and Treatment Vulnerability.

Osteoporosis, in which bones become fragile owing to low bone density and impaired bone mass, is a g...

Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.

PURPOSE: Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD...

Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet t...

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going un...

FDAA: A feature distribution-aware transferable adversarial attack method.

In recent years, the research on transferable feature-level adversarial attack has become a hot spot...

Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning.

BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kaw...

Enhancing trabecular CT scans based on deep learning with multi-strategy fusion.

Trabecular bone analysis plays a crucial role in understanding bone health and disease, with applica...

DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction.

Non-sugar sweeteners (NSSs) or artificial sweeteners have long been used as food chemicals since Wor...

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.

Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadli...

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

BACKGROUND: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prost...

Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.

In the food industry, the increasing use of automatic processes in the production line is contributi...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell ...

Long non-coding RNAs in biomarking COVID-19: a machine learning-based approach.

BACKGROUND: The coronavirus pandemic that started in 2019 has caused the highest mortality and morbi...

Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatmen...

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