scholarly journals Clinical Perspectives and Trends: Microperimetry as a trial endpoint in retinal disease

2021 ◽  
Author(s):  
Yesa Yang ◽  
Hannah Dunbar

Endpoint development trials are underway across the spectrum of retinal disease. New validated endpoints are urgently required for the assessment of emerging gene therapies and in preparation for the arrival of novel therapeutics targeting early stages of common sight-threatening conditions such as age-related macular degeneration. Visual function measures are likely to be key candidates in this search. Over the last two decades, microperimetry has been used extensively to characterize functional vision in a wide range of retinal conditions, detecting subtle defects in retinal sensitivity that precede visual acuity loss and tracking disease progression over relatively short periods. Given these appealing features, microperimetry has already been adopted as an endpoint in interventional studies, including multicenter trials, on a modest scale. A review of its use to date shows a concurrent lack of consensus in test strategy and a wealth of innovative disease and treatment-specific metrics which may show promise as clinical trial endpoints. There are practical issues to consider, but these have not held back its popularity and it remains a widely used psychophysical test in research. Endpoint development trials will undoubtedly be key in understanding the validity of microperimetry as a clinical trial endpoint, but existing signs are promising.

Ophthalmology ◽  
2016 ◽  
Vol 123 (5) ◽  
pp. 1060-1079 ◽  
Author(s):  
Karen B. Schaal ◽  
Philip J. Rosenfeld ◽  
Giovanni Gregori ◽  
Zohar Yehoshua ◽  
William J. Feuer

2018 ◽  
Vol 189 ◽  
pp. 127-138 ◽  
Author(s):  
Kimberly J. Cocce ◽  
Sandra S. Stinnett ◽  
Ulrich F.O. Luhmann ◽  
Lejla Vajzovic ◽  
Anupama Horne ◽  
...  

2021 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Kyoung Min Kim ◽  
Tae-Young Heo ◽  
Aesul Kim ◽  
Joohee Kim ◽  
Kyu Jin Han ◽  
...  

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.


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