Evaluating the impact of optical coherence tomography in diabetic retinopathy screening for an Aboriginal population

2017 ◽  
Vol 46 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Richard A O'Halloran ◽  
Angus W Turner
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Optical Coherence ◽  
Aboriginal Population ◽  
Diabetic Retinopathy Screening ◽  
The Impact ◽  
Retinopathy Screening

scholarly journals Update on Optical Coherence Tomography and Optical Coherence Tomography Angiography Imaging in Proliferative Diabetic Retinopathy

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1869
Author(s):  
Sara Vaz-Pereira ◽  
Tiago Morais-Sarmento ◽  
Michael Engelbert
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Technological Development ◽  
Imaging Techniques ◽  
Noninvasive Imaging ◽  
Vascular Density ◽  
Published Data ◽  
Optical Coherence ◽  
The Impact

Proliferative diabetic retinopathy (PDR) is a major cause of blindness in diabetic individuals. Optical coherence tomography (OCT) and OCT-angiography (OCTA) are noninvasive imaging techniques useful for the diagnosis and assessment of PDR. We aim to review several recent developments using OCT and discuss their present and potential future applications in the clinical setting. An electronic database search was performed so as to include all studies assessing OCT and/or OCTA findings in PDR patients published from 1 January 2020 to 31 May 2021. Thirty studies were included, and the most recently published data essentially focused on the higher detection rate of neovascularization obtained with widefield-OCT and/or OCTA (WF-OCT/OCTA) and on the increasing quality of retinal imaging with quality levels non-inferior to widefield-fluorescein angiography (WF-FA). There were also significant developments in the study of retinal nonperfusion areas (NPAs) using these techniques and research on the impact of PDR treatment on NPAs and on vascular density. It is becoming increasingly clear that it is critical to use adequate imaging protocols focused on optimized segmentation and maximized imaged retinal area, with ongoing technological development through artificial intelligence and deep learning. These latest findings emphasize the growing applicability and role of noninvasive imaging in managing PDR with the added benefit of avoiding the repetition of invasive conventional FA.

Diabetic Retinopathy Screening: A Systematic Review of Qualitative Literature

2021 ◽  
Author(s):  
Oluwaseun Egunsola ◽  
Laura E. Dowsett ◽  
Ruth Diaz ◽  
Michael Brent ◽  
Valeria Rac ◽  
...  
Keyword(s):  
Systematic Review ◽  
Diabetic Retinopathy ◽  
Diabetic Retinopathy Screening ◽  
Qualitative Literature ◽  
Retinopathy Screening

Digital Ophthalmology in the UK – Diabetic Retinopathy Screening and Virtual Glaucoma Clinics in the National Health Service

2020 ◽  
Vol 237 (12) ◽  
pp. 1400-1408
Author(s):  
Heinrich Heimann ◽  
Deborah Broadbent ◽  
Robert Cheeseman
Keyword(s):  
Diabetic Retinopathy ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Daily Practice ◽  
Diabetic Retinopathy Screening ◽  
Technological Advances ◽  
Imaging Results ◽  
The Uk ◽  
Retinopathy Screening

AbstractThe customary doctor and patient interactions are currently undergoing significant changes through technological advances in imaging and data processing and the need for reducing person-to person contacts during the COVID-19 crisis. There is a trend away from face-to-face examinations to virtual assessments and decision making. Ophthalmology is particularly amenable to such changes, as a high proportion of clinical decisions are based on routine tests and imaging results, which can be assessed remotely. The uptake of digital ophthalmology varies significantly between countries. Due to financial constraints within the National Health Service, specialized ophthalmology units in the UK have been early adopters of digital technology. For more than a decade, patients have been managed remotely in the diabetic retinopathy screening service and virtual glaucoma clinics. We describe the day-to-day running of such services and the doctor and patient experiences with digital ophthalmology in daily practice.

scholarly journals Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Reza Mirshahi ◽  
Pasha Anvari ◽  
Hamid Riazi-Esfahani ◽  
Mahsa Sardarinia ◽  
Masood Naseripour ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Healthy Subjects ◽  
Optical Coherence Tomography Angiography ◽  
Validation Dataset ◽  
Diabetic Patients ◽  
Dice Similarity Coefficient ◽  
Optical Coherence ◽  
Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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