Endocrinology

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

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How to Demonstrate the Glucose Specificity of a Non-Invasive CGM: A Case Study of the SKAMo-2 Clinical Trial and Neogly™

Abstract Background: Every non-invasive continuous glucose monitoring (NI-CGM) technology introduced...

CMCNet: Aligning Ultrasound Image Embeddings with Textual TI-RADS Representations for Fine-Grained Thyroid Classification

Ultrasound is the primary imaging modality for assessing thyroid nodules, and the ACR TI-RADS framew...

Auditable agentic AI for evidence-grounded thyroid ultrasound diagnosis and reporting

Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratificat...

Protective effects of testosterone replacement therapy on brain tumor outcomes: the Mayo Clinic Experience

BackgroundBiological sex and endocrine signaling influence cancer biology, immune response, and ther...

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Deep Learning of Fluorescence Lifetime Imaging Ophthalmoscopy for Type 2 Diabetes Classification

Purpose: To evaluate whether fluorescence lifetime imaging ophthalmoscopy (FLIO) combined with deep ...

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 transl...

Reassessing the Feasibility of PPG-Based Non-Invasive Blood Glucose Level Estimation

Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promis...

Self-supervised DXA representations encode multi-system disease risk, biological aging and heritability

Whole-body dual-energy X-ray absorptiometry (DXA) scans are routinely acquired to measure bone densi...

Metabolic brain networks switch between a sparsely connected baseline and highly integrated states to support cognition

The human brain achieves cognitive flexibility by rapidly switching between large-scale functional n...

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially...

Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft...

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A digital health approach for identifying polyendocrine metabolic ovarian syndrome using machine learning and body temperature

Background Polyendocrine Metabolic Ovarian Syndrome (PMOS), formerly known as Polycystic Ovary Syndr...

Forecasting Trajectories of Physiological Mechanics with Sparse Clinical Data Using a Data Assimilation and Machine Learning Hybrid

Clinical decisions for determining optimal patient-specific interventions are complicated prediction...

Adversarial Validation Reveals Diagnostic Workflow Leakage in PCOS Machine Learning Models

Background: Machine-learning models for polycystic ovary syndrome (PCOS) and other conditions freque...

GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing d...

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because d...

Coupled Cell-Intrinsic and Microenvironmental Heterogeneity Drives Divergent Trajectories in Castration-Resistant Prostate Cancer

Castration-resistant prostate cancer emerges from coupling between cell-intrinsic heterogeneity and ...

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