Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
Deep learning-enabled medical computer vision
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …
potential for many fields—including medicine—to benefit from the insights that AI techniques …
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
The simultaneous maturation of multiple digital and telecommunications technologies in
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
Applications of deep learning in fundus images: A review
The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …
importance. Due to its powerful performance, deep learning is becoming more and more …
The myth of generalisability in clinical research and machine learning in health care
An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …
machine learning in health care can overlook situations in which machine learning might …
Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study
P Ruamviboonsuk, R Tiwari, R Sayres… - The Lancet Digital …, 2022 - thelancet.com
Background Diabetic retinopathy is a leading cause of preventable blindness, especially in
low-income and middle-income countries (LMICs). Deep-learning systems have the …
low-income and middle-income countries (LMICs). Deep-learning systems have the …
Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
Visually impaired and blind people due to diabetic retinopathy were 2.6 million in 2015 and
estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is …
estimated to be 3.2 million in 2020 globally. Though the incidence of diabetic retinopathy is …
[HTML][HTML] Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification
Diabetic Retinopathy (DR) is the most common cause of eyesight loss that affects millions of
people worldwide. Although there are recognized screening procedures for detecting the …
people worldwide. Although there are recognized screening procedures for detecting the …
Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study
Background Deep learning is a novel machine learning technique that has been shown to
be as effective as human graders in detecting diabetic retinopathy from fundus photographs …
be as effective as human graders in detecting diabetic retinopathy from fundus photographs …
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs
F Li, Y Wang, T Xu, L Dong, L Yan, M Jiang, X Zhang… - Eye, 2022 - nature.com
Objectives To present and validate a deep ensemble algorithm to detect diabetic retinopathy
(DR) and diabetic macular oedema (DMO) using retinal fundus images. Methods A total of …
(DR) and diabetic macular oedema (DMO) using retinal fundus images. Methods A total of …