Diabetes is a chronic metabolic disorder that affects an estimated 463 million people
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise
care for people with diabetes and adapt treatments for complex presentations. However, the …

Automated detection of curvilinear structures, eg, blood vessels or nerve fibres, from medical
and biomedical images is a crucial early step in automatic image interpretation associated to …

People living with type 1 diabetes (T1D) require lifelong self-management to maintain
glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with …

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to
diabetes and diabetic complications, and related substantial economic burden make …

Artificial intelligence (AI) is an emerging field which could make an intelligent healthcare
model a reality and has been garnering traction in the field of medicine, with promising …

There is growing evidence of excess peripheral neuropathy in pre-diabetes. We aimed to
determine its prevalence, including the impact of diagnostic methodology on prevalence …

Diabetic peripheral neuropathy (DPN) is the most common complication of both type 1 and 2
diabetes. As a result, neuropathic pain, diabetic foot ulcers and lower-limb amputations …

Artificial intelligence can use real-world data to create models capable of making predictions
and medical diagnosis for diabetes and its complications. The aim of this commentary article …

To demonstrate the identification of corneal diseases using a novel deep learning algorithm.
A novel hierarchical deep learning network, which is composed of a family of multi-task multi …