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Subcategories: Diabetes Menopause Osteoporosis Steroids Thyroid

Showing 2122-2142 of 5,041 articles

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing t...

Aug 2019 PMID: 31415986

An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network.

Due to insufficient samples, the generalization performance of deep network is insufficient. In orde...

Aug 2019 PMID: 31407110

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes...

Aug 2019 PMID: 31421276

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

BACKGROUND: Hyperglycemia or high blood glucose during surgery is associated with poor postoperative...

Aug 2019 PMID: 31398727

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (C...

Aug 2019 PMID: 31606116

Sex Differences in Diabetes Prevalence, Comorbidities, and Health Care Utilization among American Indians Living in the Northern Plains.

BACKGROUND: The American Indian (AI) population experiences significant diet-related health disparit...

Aug 2019 PMID: 32258998

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, w...

Aug 2019 PMID: 31605882

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinic...

Aug 2019 PMID: 31375883

Retinal image assessment using bi-level adaptive morphological component analysis.

The automated analysis of retinal images is a widely researched area which can help to diagnose seve...

Jul 2019 PMID: 31606110

Supplementation of OmniGen-AF improves the metabolic response to a glucose tolerance test in beef heifers.

This study determined whether feeding the immunomodulating supplement, OmniGen-AF, to feedlot heifer...

Jul 2019 PMID: 32704915

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. ...

Jul 2019 PMID: 31369389

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.

For people with Type 1 diabetes (T1D), forecasting of blood glucose (BG) can be used to effectively ...

Jul 2019 PMID: 31369390

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluatio...

Jul 2019 PMID: 31362884

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.

Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from m...

Jul 2019 PMID: 31606114

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) r...

Jul 2019 PMID: 31383477

Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China, especially in urban ...

Jul 2019 PMID: 31346994

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification an...

Jul 2019 PMID: 31336327

Browse Categories

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network.

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Sex Differences in Diabetes Prevalence, Comorbidities, and Health Care Utilization among American Indians Living in the Northern Plains.

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Retinal image assessment using bi-level adaptive morphological component analysis.

Supplementation of OmniGen-AF improves the metabolic response to a glucose tolerance test in beef heifers.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults.

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

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