Menopause

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper.

Effective drug development for bone-related diseases, such as osteoporosis and metastasis, is hindered by the lack of physiologically relevant in vitro models. Traditional platforms, including standard tissue culture plastic, fail to replicate the structural and functional complexity of the natural bone extracellular matrix. Recently, osteoid-mimicking demineralized bone paper (DBP), which preserv...

Predictive modelling of knee osteoporosis.

OBJECTIVE: The objective of this research was to develop a machine learning-based predictive model for osteoporosis screening using demographic and clinical data, including T-scores derived from calcaneus Quantitative Ultrasound (QUS). The study aimed to offer a cost-effective and accessible alternative to Dual-Energy X-ray Absorptiometry (DXA) scans, especially in resource-constrained settings.

Integration of intratumoral and peritumoral CT radiomic features with machine learning algorithms for predicting induction therapy response in locally advanced non-small cell lung cancer.

OBJECTIVES: To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models u...

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessme...

Deep learning-based evaluation of panoramic radiographs for osteoporosis screening: a systematic review and meta-analysis.

BACKGROUND: Osteoporosis is a complex condition that drives research into its causes, diagnosis, treatment, and prevention, significantly affecting pa...

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without ...

PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.

OBJECTIVES: Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitat...

Global Cross-Entropy Loss for Deep Face Recognition.

Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample featu...

Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.

INTRODUCTION: Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fractur...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potent...

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and ...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth fa...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major...

EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.

Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA seq...

Accurate phenotyping of luminal A breast cancer in magnetic resonance imaging: A new 3D CNN approach.

Breast cancer (BC) remains a predominant and deadly cancer in women worldwide. By 2040, projections indicate that more than 3 million new cases of bre...

HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-Body Mesh Recovery.

Recovering whole-body mesh by inferring the abstract pose and shape parameters from visual content can obtain 3D bodies with realistic structures. How...

Effectiveness of Generative Artificial Intelligence-Driven Responses to Patient Concerns in Long-Term Opioid Therapy: Cross-Model Assessment.

While long-term opioid therapy is a widely utilized strategy for managing chronic pain, many patients have understandable questions and concerns rega...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offers a superior solution to the lack of specificity i...

Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer.

CDK4/6 inhibitors in combination with endocrine therapy are widely used to treat HR+/HER2- metastatic breast cancer leading to improved progression-fr...

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural...