Diabetes

Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.

AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from ...

Updates of precision medicine in type 2 diabetes.

Diabetes mellitus is prevalent worldwide and affects 1 in 10 adults. Despite the successful developm...

A deep learning nomogram of continuous glucose monitoring data for the risk prediction of diabetic retinopathy in type 2 diabetes.

Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors ...

Role of calibration in uncertainty-based referral for deep learning.

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging t...

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patien...

Robot-assisted lateral pelvic lymph node dissection in patients with advanced rectal cancer: a single-center experience of 65 cases.

The treatment of lateral pelvic lymph node (LPLN) metastasis of rectal cancer has evolved because of...

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal ...

A genome-wide association study of childhood adiposity and blood lipids.

The rising prevalence of childhood obesity and dyslipidaemia is a major public health concern due t...

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threat...

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main...

Prescribing patterns of SGLT-2 inhibitors for patients with heart failure: A two-center analysis.

BACKGROUND: Sodium glucose co-transporter 2 inhibitors (SGLT2i) have been proven to reduce the combi...

Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms.

PURPOSE: A deep learning framework to differentiate glaucomatous optic disc changes due to glaucomat...

Effect of pneumatic leg compression on post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy: a double-blind randomised controlled trial.

Post-induction hypotension is common and associated with postoperative complications. We hypothesise...

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods.

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may...

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...

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 fail...

Using ChatGPT to Predict the Future of Diabetes Technology.

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 recurre...

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 support...

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. ...

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