Obstetrics & Gynecology

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

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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 bi...

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

Molecular recognition events between proteins drive biological processes in living systems. However,...

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 classifi...

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). ...

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 th...

Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes.

Currently applicable models for predicting live birth outcomes in patients who received assisted rep...

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.

Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predic...

XGBoost-based nomogram for predicting lymph node metastasis in endometrial carcinoma.

This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in ...

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the compl...

iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting f...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagno...

Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.

Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis...

Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.

BACKGROUND: Osteoporosis is one of the most common metabolic diseases that is characterized by a dec...

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Fall is a common adverse event among older adults. This study aimed to identify essential fall facto...

Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks.

BACKGROUND: Osteoporosis (OP) is a condition that significantly decreases bone density and strength,...

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