Diabetes

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was o...

Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study.

Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic h...

The impact of diabetes mellitus on pelvic organ prolapse recurrence after robotic sacrocolpopexy.

INTRODUCTION AND HYPOTHESIS: Data examining the effect of diabetes mellitus (DM) on prolapse recurrence after sacrocolpopexy (SCP) is limited. The pri...

Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the pract...

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.

Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on t...

Deep learning synthetic angiograms for individuals unable to undergo contrast-guided laser treatment in aggressive retinopathy of prematurity.

Deep learning-based hemorrhage detection for diabetic retinopathy screening.

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy th...

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existi...

Response to: 'Deep learning synthetic angiograms for individuals unable to undergo contrast-guided laser treatment in aggressive retinopathy of prematurity'.

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.

OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation ...

Nailfold capillaroscopy and deep learning in diabetes.

OBJECTIVE: To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and ...

Prediction of ethanol fermentation under stressed conditions using yeast morphological data.

A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and ...

Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1-Deficient Mice.

Metabolic flux augmentation via glucose transport activation may be desirable in glucose transporter 1 (Glut1) deficiency syndrome (G1D) and dementia,...

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglyc...

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individuali...

A Deep-Learning-Based Collaborative Edge-Cloud Telemedicine System for Retinopathy of Prematurity.

Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis ...

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning.

The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, hav...

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance.

BACKGROUND: Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical...

Acute Intraoperative Hyperkalemia During Robot-Assisted Radical Cystectomy: A Case Report.

A 50-year-old man with muscle-invasive bladder cancer was scheduled for a robotic radical cystectomy. Four hours into the surgery, his electrocardiogr...

A Machine Learning Model for Prediction of Amputation in Diabetics.

BACKGROUND: Diabetic foot ulcer (DFU) and the resulting lower extremity amputation are associated with a poor survival prognosis. The objective of thi...