Endocrinology

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

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Optimization of mid-infrared noninvasive blood-glucose prediction model by support vector regression coupled with different spectral features.

Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employe...

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets.

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secr...

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques.

The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visibl...

Group-informed attentive framework for enhanced diabetes mellitus progression prediction.

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramo...

RISK FACTORS FOR ACUTE KIDNEY INJURY ASSOCIATED WITH SEVERE HYPOTHYROIDISM.

OBJECTIVE: This study aims to investigate the factors affecting development of acute kidney injury (...

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prev...

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.

OBJECTIVES: This study investigated the diagnostic performance of dual-energy computed tomography (C...

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders aff...

Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning.

Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diag...

Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.

BACKGROUND: Various surrogate markers of insulin resistance have been developed, capable of predicti...

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding ...

Machine-learning-guided recognition of α and β cells from label-free infrared micrographs of living human islets of Langerhans.

Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting β...

Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.

BACKGROUND/AIMS: Support vector machine-based automated grading (known as iGradingM) has been shown ...

Raman spectroscopy with an improved support vector machine for discrimination of thyroid and parathyroid tissues.

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectros...

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results.

Over the last few years, there has been an increasing focus on integrating artificial intelligence (...

Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression...

Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.

BACKGROUND: Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformatio...

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