Menopause

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 diagnosis are using multi-omics data to better understand the complexity of breast cancer.ObjectiveThe new strategy laid out in this work, called "Hybrid-OmniSeq," is a deep learning-based multi-omics data analysis technology that uses molecular subtypes of breast cancer gene to increase the precision and...

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 decrease in bone density and a loss of the quality of the bone structure. The use of deep learning in the prediction of osteoporosis can provide a non-invasive, cost-effective, and efficient approach. The aim of this study is to investigate the diagnostic accuracy of deep learning in the prediction of ...

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 can be vital in handling the disease efficiently....

The Effects of Perfluorooctanesulfonic acid (PFOS) on Human Umbilical Vein Endothelial Cells (HUVECs) Proliferation and Gene Expression and its Implications on Fetal Development.

Polyfluoro-alkyl substances (PFAS) are widely distributed environmental contaminants linked to human toxicity and developmental delays, especially low...

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 factors and develop a machine learning-based prediction...

Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks.

: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imagi...

Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesteron...

A Novel Real-time Phase Prediction Network in EEG Rhythm.

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize th...

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, often remaining undetected until the occurrence o...

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence.

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and expl...

Artificial intelligence meets the world experts; updates and novel therapies in autoimmunity - The 14th international congress on autoimmunity 2024 (AUTO14), Ljubljana.

The bi-annual international congress on autoimmunity is a huge opportunity for the medical community to discuss the latest updates in the field. Durin...

Application of Machine Learning to Osteoporosis and Osteopenia Screening Using Hand Radiographs.

PURPOSE: Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing ...

Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters.

Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, t...

MRI denoising with a non-blind deep complex-valued convolutional neural network.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the ma...

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model.

INTRODUCTION: The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of old...

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing ...

Utilizing artificial intelligence to determine bone mineral density using spectral CT.

BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring ded...

Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.

PURPOSE: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomark...

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adu...

A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different view...