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 …
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 …
A deep learning system for detecting diabetic retinopathy across the disease spectrum
Retinal screening contributes to early detection of diabetic retinopathy and timely treatment.
To facilitate the screening process, we develop a deep learning system, named DeepDR …
To facilitate the screening process, we develop a deep learning system, named DeepDR …
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 …
Deepdrid: Diabetic retinopathy—grading and image quality estimation challenge
We described a challenge named" Diabetic Retinopathy (DR)—Grading and Image Quality
Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and …
Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and …
Deep learning in ophthalmology: the technical and clinical considerations
The advent of computer graphic processing units, improvement in mathematical models and
availability of big data has allowed artificial intelligence (AI) using machine learning (ML) …
availability of big data has allowed artificial intelligence (AI) using machine learning (ML) …
Artificial intelligence for diabetic retinopathy screening: a review
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 …
population, this poses significant challenge to perform diabetic retinopathy (DR) screening …
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
Artificial intelligence technology has advanced rapidly in recent years and has the potential
to improve healthcare outcomes. However, technology uptake will be largely driven by …
to improve healthcare outcomes. However, technology uptake will be largely driven by …
Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study
Background Radical measures are required to identify and reduce blindness due to
diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated …
diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated …
Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology
The lifestyle of modern society has changed significantly with the emergence of artificial
intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent …
intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent …