Deep learning for diabetes: a systematic review
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 …
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …
Artificial intelligence for diabetes care: current and future prospects
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 …
care for people with diabetes and adapt treatments for complex presentations. However, the …
CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging
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 …
and biomedical images is a crucial early step in automatic image interpretation associated to …
Enhancing self-management in type 1 diabetes with wearables and deep learning
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 …
glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with …
Early detection of diabetic peripheral neuropathy: a focus on small nerve fibres
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 …
diabetes. As a result, neuropathic pain, diabetic foot ulcers and lower-limb amputations …
Diabetic corneal neuropathy
Diabetic keratopathy (DK) is a common, but underdiagnosed, ocular complication of
diabetes mellitus (DM) that has a significant economic burden. It is characterised by …
diabetes mellitus (DM) that has a significant economic burden. It is characterised by …
Prevalence of peripheral neuropathy in pre-diabetes: a systematic review
V Kirthi, A Perumbalath, E Brown, S Nevitt… - BMJ Open Diabetes …, 2021 - drc.bmj.com
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 …
determine its prevalence, including the impact of diagnostic methodology on prevalence …
Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs
H Gu, Y Guo, L Gu, A Wei, S **e, Z Ye, J Xu, X Zhou… - Scientific reports, 2020 - nature.com
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 …
A novel hierarchical deep learning network, which is composed of a family of multi-task multi …
Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion
The conventional diagnostic process and tools of cardiovascular autonomic neuropathy
(CAN) can easily identify the two main categories of the condition: severe/definite CAN and …
(CAN) can easily identify the two main categories of the condition: severe/definite CAN and …
Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes
Aims/hypothesis We aimed to develop an artificial intelligence (AI)-based deep learning
algorithm (DLA) applying attribution methods without image segmentation to corneal …
algorithm (DLA) applying attribution methods without image segmentation to corneal …