Diabetes

An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study.

BACKGROUND: Nearly one-third of patients with diabetes are poorly controlled (hemoglobin A≥9%). Identifying at-risk individuals and providing them with effective treatment is an important strategy for preventing poor control.

FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are int...

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study.

BACKGROUND: Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and anal...

Automatic Identification of Ultrasound Images of the Tibial Nerve in Different Ankle Positions Using Deep Learning.

Peripheral nerve tension is known to be related to the pathophysiology of neuropathy; however, assessing this tension is difficult in a clinical setti...

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling ap...

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmol...

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 a diabetic retinopathy screening programme improve...

Updates of precision medicine in type 2 diabetes.

Diabetes mellitus is prevalent worldwide and affects 1 in 10 adults. Despite the successful development of glucose-lowering drugs, such as glucagon-li...

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 related to diabetic retinopathy (DR). However, the...

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

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in s...

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 patients with diabetes. Yet, statin use is concerningly ...

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 technical difficulties from open surgery to lapar...

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 disease and elevated glycated haemoglobin. This st...

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 to its association with morbidity and mortality in ...

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-threatening diseases that are leading causes of vision l...

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

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Ev...

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 combined risk of cardiovascular death and hospitalizati...

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 glaucomatous optic neuropathy (GON) from non-glaucomatous o...

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 hypothesised that pneumatic leg compression reduces post-indu...

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 progress to end-stage renal disease with a high m...