Super-resolution ultrasound imaging

K Christensen-Jeffries, O Couture, PA Dayton… - Ultrasound in medicine …, 2020 - Elsevier
The majority of exchanges of oxygen and nutrients are performed around vessels smaller
than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer …

Artificial intelligence for diabetic retinopathy screening: a review

A Grzybowski, P Brona, G Lim, P Ruamviboonsuk… - Eye, 2020 - nature.com
Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing
population, this poses significant challenge to perform diabetic retinopathy (DR) screening …

The value of automated diabetic retinopathy screening with the EyeArt system: a study of more than 100,000 consecutive encounters from people with diabetes

M Bhaskaranand, C Ramachandra, S Bhat… - Diabetes technology …, 2019 - liebertpub.com
Background: Current manual diabetic retinopathy (DR) screening using eye care experts
cannot scale to screen the growing population of diabetes patients who are at risk for vision …

[HTML][HTML] Advances in retinal imaging and applications in diabetic retinopathy screening: a review

BJ Fenner, RLM Wong, WC Lam, GSW Tan… - Ophthalmology and …, 2018 - Springer
Rising prevalence of diabetes worldwide has necessitated the implementation of population-
based diabetic retinopathy (DR) screening programs that can perform retinal imaging and …

Deep learning–based algorithms in screening of diabetic retinopathy: a systematic review of diagnostic performance

KB Nielsen, ML Lautrup, JKH Andersen… - Ophthalmology …, 2019 - Elsevier
Topic Diagnostic performance of deep learning–based algorithms in screening patients with
diabetes for diabetic retinopathy (DR). The algorithms were compared with the current gold …

[HTML][HTML] Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: the RAIDERS randomized trial

W Mathenge, N Whitestone, J Nkurikiye… - Ophthalmology …, 2022 - Elsevier
Purpose This trial was designed to determine if artificial intelligence (AI)-supported diabetic
retinopathy (DR) screening improved referral uptake in Rwanda. Design The Rwanda …

A multi-label deep learning model with interpretable Grad-CAM for diabetic retinopathy classification

H Jiang, J Xu, R Shi, K Yang, D Zhang… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple
types of lesions which provided strong evidence for the ophthalmologists to make diagnosis …

[HTML][HTML] The role of telemedicine, in-home testing and artificial intelligence to alleviate an increasingly burdened healthcare system: Diabetic retinopathy

J Pieczynski, P Kuklo, A Grzybowski - Ophthalmology and therapy, 2021 - Springer
In the presence of the ever-increasing incidence of diabetes mellitus (DM), the prevalence of
diabetic eye disease (DED) is also growing. Despite many improvements in diabetic care …

The evolution of diabetic retinopathy screening programmes: a chronology of retinal photography from 35 mm slides to artificial intelligence

J Huemer, SK Wagner, DA Sim - Clinical Ophthalmology, 2020 - Taylor & Francis
As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of
diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment …

[HTML][HTML] Fundamental principles of an effective diabetic retinopathy screening program

P Lanzetta, V Sarao, PH Scanlon, J Barratt, M Porta… - Acta …, 2020 - Springer
Background Diabetic retinopathy (DR) is the leading cause of blindness among working-age
adults worldwide. Early detection and treatment are necessary to forestall vision loss from …