Endocrinology

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

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Classification of glucose-level in deionized water using machine learning models and data pre-processing technique.

Accurate monitoring of glucose levels is essential in the field of diabetes detection and prevention...

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...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early c...

Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation.

BACKGROUND: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision ...

Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodit...

Comparative evaluation of ChatGPT-4, ChatGPT-3.5 and Google Gemini on PCOS assessment and management based on recommendations from the 2023 guideline.

CONTEXT: Artificial intelligence (AI) is increasingly utilized in healthcare, with models like ChatG...

A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning.

Diabetes foot ulcer (DFU) is one of the common complications of diabetes patients, which may lead to...

Diabetic retinopathy detection via deep learning based dual features integrated classification model.

BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent b...

Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI.

For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the dis...

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hype...

Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

The morphological diagnosis of thyroid gland neoplasms presents a dual challenge: it requires the ex...

The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.

OBJECTIVE: The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultra...

Exploring acetylation-related gene markers in polycystic ovary syndrome: insights into pathogenesis and diagnostic potential using machine learning.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is a prevalent cause of menstrual irregularities and inf...

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