The study's objective is to evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure, on standard retinal fundus photographs. A DLS was trained to automatically classify papilledema severity in 965 patients (2103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1052 photographs with mild/moderate papilledema (MP) and 1051 photographs with severe papilledema (SP) classified by a panel of experts, and the performance of the DLS was tested in 111 patients (214 photographs, 92 with MP and 122 with SP). In this dataset, we provide illustrative examples of...
The recently validated BONSAI deep learning system (DLS) was able to distinguish papilledema from no...
The quality of ocular fundus photographs can affect the accuracy of the morphologic assessment of th...
We have attempted to reproduce the results in Development and validation of a deep learning algorith...
Objective: To evaluate the performance of a deep learning system (DLS) in classifying the severity o...
BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use ...
BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmos-copy. The use...
Background Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use o...
Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of int...
The FOTO-ED studies showed that ED providers (EDPs) poorly recognized relevant ocular funduscopic fi...
Objective: To compare the diagnostic performance of an artificial intelligence deep learning system ...
Differentiating pseudopapilledema and papilledema in children represents a significant diagnostic di...
Background: To date, deep learning-based detection of optic disc abnormalities in color fundus photo...
We have attempted to reproduce the results in Development and validation of a deep learning algorith...
Objective: To compare the diagnostic performance of an artificial intelligence deep learning system ...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accur...
The recently validated BONSAI deep learning system (DLS) was able to distinguish papilledema from no...
The quality of ocular fundus photographs can affect the accuracy of the morphologic assessment of th...
We have attempted to reproduce the results in Development and validation of a deep learning algorith...
Objective: To evaluate the performance of a deep learning system (DLS) in classifying the severity o...
BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use ...
BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmos-copy. The use...
Background Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use o...
Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of int...
The FOTO-ED studies showed that ED providers (EDPs) poorly recognized relevant ocular funduscopic fi...
Objective: To compare the diagnostic performance of an artificial intelligence deep learning system ...
Differentiating pseudopapilledema and papilledema in children represents a significant diagnostic di...
Background: To date, deep learning-based detection of optic disc abnormalities in color fundus photo...
We have attempted to reproduce the results in Development and validation of a deep learning algorith...
Objective: To compare the diagnostic performance of an artificial intelligence deep learning system ...
Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accur...
The recently validated BONSAI deep learning system (DLS) was able to distinguish papilledema from no...
The quality of ocular fundus photographs can affect the accuracy of the morphologic assessment of th...
We have attempted to reproduce the results in Development and validation of a deep learning algorith...