Endocrinology

Osteoporosis

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

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Development of a Deep Learning Model for Opportunistic Screening of Osteoporosis using Chest Radiographs

Purpose Prevention and early detection of osteoporosis remains a global challenge, more so in regions like the Philippines where screening barriers exist. Chest x-rays meanwhile are relatively inexpensive, and more frequently done, and therefore can be used for opportunistic screening. This study aimed to develop a deep learning model for osteoporosis detection from chest x-rays using DXA as the g...

MetaFemina: development and evaluation of a large language model-assisted platform for automated meta-analysis of nutritional exposures and breast, ovarian, and uterine cancer risk

Objective To develop and evaluate an automated large language model (LLM)-based framework for conducting meta-analyses of nutrition-related exposures and the risk of breast, ovarian, and uterine cancers. Design We developed MetaFemina, an automated evidence-synthesis pipeline for women's cancers that integrates keyword-based literature retrieval, LLM-assisted evidence extraction, and random-effect...

MetaboLLM: a metabolomics-specialized large language model for biochemical knowledge integration and predictive metabolite graph construction

Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations. We developed ...

Aug 6 2026 2608.06253v1
Exploratory Network Analysis of Oral Bacteria Taste Signaling Autophagy Crosstalk in Oral Squamous Cell Carcinoma and Multi-Target Ligand Design for the MAPK1 STAT3 mTOR Axis

G protein-coupled receptor (GPCR) signaling represents a critical interface between oral bacteria and host cellular regulation in oral squamous cell c...

Characterizing Functional Clusters of V4 Neurons in Digital Twins

Neurons in primate visual cortical area V4 display tuning for multiple visual features, including color, shape, texture, and depth. Whether and how th...

Closed-loop control of in vitro neuronal activity using reinforcement learning after in silico pre-training

Controlling specific neuronal dynamics with electrical stimulation is critical for therapeutic neuromodulation, yet deriving optimal control policies ...

Decoding the oxytocinergic and behavioral signatures of milk ejection

Oxytocin-mediated milk ejection (ME) is pivotal to effective breastfeeding and productive health, yet behaviorally decoding and revealing neural mecha...

A Deep Learning Framework for Biomarker Segmentation and Classification in Traumatic Brain Injury

Traumatic brain injury (TBI) triggers widespread biomarker activation, including astrocytic markers such as glial fibrillary acidic protein (GFAP) and...

Machine Learning Models for Osteoporosis Prediction: A Systematic Review and Meta-Analysis

Purpose The application of machine learning (ML) to osteoporosis prediction has expanded rapidly, yet no comprehensive meta-analysis has synthesized t...

Generative AI Models Reveal Dynamic Views of Aging (DyViA) Phenotypes in Healthy Individuals

Background and objectives: In recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, w...

Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans

Background: Coronary artery calcium (CAC) is an established measure of coronary atherosclerosis from computed tomography (CT). While deep learning (DL...

HyTrax: Deep Sequential Modeling of Serial Musculoskeletal Measurements for Fracture Prediction in the Women's Health Initiative with External Evaluation in the Framingham Heart Study

The clinical utility of monitoring longitudinal changes in musculoskeletal trajectories, including bone mineral density (BMD), muscle strength, height...

Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images

Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimens...

Jun 30 2026 2606.31394v1
Validation of an Artificial Intelligence-Assisted Mobile Application for Dietary Oxalate Assessment in Kidney Stone Prevention

Background: Calcium oxalate nephrolithiasis is the most common type of kidney stone disease. Dietary oxalate intake is an important modifiable factor....

Predicting Mouse Lifespan-Extending Chemical Compounds with Machine Learning

Pharmacological interventions targeting the biological processes of ageing hold significant potential to extend healthspan and promote longevity. This...

Controlling metal-carbonate phase, form, and function through de novo protein design

Biomineralization enables living systems to construct hybrid materials by controlling the location, orientation, and polymorph of inorganic crystals w...

Learning using switching synaptic plasticity rules

Hebbian-like learning has been repeatedly confirmed experimentally, yet computational models usually require non-local signals, such as backpropagatin...

Neural Innervation Invigorates Yolk Sac Biological Functions beyond Nutrient Reservoir during Zebrafish Embryo Development

The zebrafish yolk sac (YS) is traditionally viewed as a nutrient reservoir. By reconstructing the complete progression of embryonic neural developmen...

Precise calcium-to-spike inference using biophysical generative models

The intramolecular dynamics of fluorescent calcium indicators distort the relationship between calcium signals and action potentials (spikes), hamperi...

Smart AI-Powered Machine Learning Risk Assessment for Early Osteoporosis Detection for Women Bone Health

Osteoporosis is often called a silent disease because it progresses without symptoms until a fracture occurs, posing a serious, yet frequently overloo...

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