Retinal Fundus Imaging in Mouse Models of Retinal Diseases

2019 ◽  
pp. 253-283 ◽  
Author(s):  
Anne F. Alex ◽  
Maged Alnawaiseh ◽  
Peter Heiduschka ◽  
Nicole Eter
Keyword(s):  
Mouse Models ◽  
Retinal Diseases ◽  
Fundus Imaging ◽  
Retinal Fundus

scholarly journals Structural evaluation in inherited retinal diseases

2021 ◽  
pp. bjophthalmol-2021-319228
Author(s):  
Malena Daich Varela ◽  
Burak Esener ◽  
Shaima A Hashem ◽  
Thales Antonio Cabral de Guimaraes ◽  
Michalis Georgiou ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Fundus Autofluorescence ◽  
Standard Of Care ◽  
Laser Speckle ◽  
Fundus Photography ◽  
Laser Speckle Flowgraphy ◽  
Retinal Diseases ◽  
Autofluorescence Imaging ◽  
Structural Evaluation ◽  
Fundus Imaging

Ophthalmic genetics is a field that has been rapidly evolving over the last decade, mainly due to the flourishing of translational medicine for inherited retinal diseases (IRD). In this review, we will address the different methods by which retinal structure can be objectively and accurately assessed in IRD. We review standard-of-care imaging for these patients: colour fundus photography, fundus autofluorescence imaging and optical coherence tomography (OCT), as well as higher-resolution and/or newer technologies including OCT angiography, adaptive optics imaging, fundus imaging using a range of wavelengths, magnetic resonance imaging, laser speckle flowgraphy and retinal oximetry, illustrating their utility using paradigm genotypes with on-going therapeutic efforts/trials.

scholarly journals A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

10.2196/28868 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e28868
Author(s):  
Eugene Yu-Chuan Kang ◽  
Ling Yeung ◽  
Yi-Lun Lee ◽  
Cheng-Hsiu Wu ◽  
Shu-Yen Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multimodal Imaging ◽  
Vascular Diseases ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Retinal Diseases ◽  
Ophthalmic Imaging ◽  
Retinal Fundus ◽  
Deep Learning Model

Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

Automatic Diagnosis for Glaucoma Fusing Multi-Feature and Multi-Classifier

2020 ◽  
Vol 10 (7) ◽  
pp. 1540-1546
Author(s):  
Xiaomei Xu ◽  
Xiaobo Lai ◽  
Yanli Liu
Keyword(s):  
Cross Validation ◽  
Maximum Intensity ◽  
Eye Disease ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Time Cost ◽  
Fusion Model ◽  
Glaucoma Diagnosis ◽  
Retinal Fundus ◽  
Fold Cross Validation

Glaucoma is a chronic and irreversible eye disease leading to blindness, and early detection is particularly important for its diagnosis and treatment. To improve the performance of automatic glaucoma diagnosis, a method based on multi-feature and multi-classifier is proposed. Firstly, an average histogram is obtained for each channel and ophthalmic condition, and 6 features are extracted from the average histogram with the average count of pixels and their maximum intensity value. Secondly, the optimal features combination is screened for each classifier with 10-fold cross-validation. Finally, the three optimal classifiers and their optimal features combination are fused with the principle of democratic voting. With the 10-fold cross-validation algorithm, the fusion model was evaluated on Local and HRF dataset, that achieved accuracy of 91.8% and 93.3%, sensitivity of 86.9% and 86.7%, specificity of 96.7% and 100%, AUC of 0.953 and 0.978, time cost of 1.0 s and 3.9 s per image, respectively. Simulation results show that the proposed method is of high accuracy and generality. It can effectively classify the retinal fundus images and provide technical support for the clinical diagnosis of retinal diseases.

scholarly journals The Interplay between Peripherin 2 Complex Formation and Degenerative Retinal Diseases

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 784 ◽  
Author(s):  
Lars Tebbe ◽  
Mashal Kakakhel ◽  
Mustafa S. Makia ◽  
Muayyad R. Al-Ubaidi ◽  
Muna I. Naash
Keyword(s):  
Complex Formation ◽  
Mouse Models ◽  
Outer Segment ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Fine Tuning ◽  
Current Status ◽  
Retinal Diseases ◽  
Choroidal Vasculature ◽  
And Function

Peripherin 2 (Prph2) is a photoreceptor-specific tetraspanin protein present in the outer segment (OS) rims of rod and cone photoreceptors. It shares many common features with other tetraspanins, including a large intradiscal loop which contains several cysteines. This loop enables Prph2 to associate with itself to form homo-oligomers or with its homologue, rod outer segment membrane protein 1 (Rom1) to form hetero-tetramers and hetero-octamers. Mutations in PRPH2 cause a multitude of retinal diseases including autosomal dominant retinitis pigmentosa (RP) or cone dominant macular dystrophies. The importance of Prph2 for photoreceptor development, maintenance and function is underscored by the fact that its absence results in a failure to initialize OS formation in rods and formation of severely disorganized OS membranous structures in cones. Although the exact role of Rom1 has not been well studied, it has been concluded that it is not necessary for disc morphogenesis but is required for fine tuning OS disc size and structure. Pathogenic mutations in PRPH2 often result in complex and multifactorial phenotypes, involving not just photoreceptors, as has historically been reasoned, but also secondary effects on the retinal pigment epithelium (RPE) and retinal/choroidal vasculature. The ability of Prph2 to form complexes was identified as a key requirement for the development and maintenance of OS structure and function. Studies using mouse models of pathogenic Prph2 mutations established a connection between changes in complex formation and disease phenotypes. Although progress has been made in the development of therapeutic approaches for retinal diseases in general, the highly complex interplay of functions mediated by Prph2 and the precise regulation of these complexes made it difficult, thus far, to develop a suitable Prph2-specific therapy. Here we describe the latest results obtained in Prph2-associated research and how mouse models provided new insights into the pathogenesis of its related diseases. Furthermore, we give an overview on the current status of the development of therapeutic solutions.

scholarly journals Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

2021 ◽  
Vol 11 (4) ◽  
pp. 1754
Author(s):  
Jooyoung Kim ◽  
Sojung Go ◽  
Kyoungjin Noh ◽  
Sangjun Park ◽  
Soochahn Lee
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Experimental Evaluation ◽  
Image Texture ◽  
Peripheral Retina ◽  
Image Artifacts ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Learning Methods ◽  
Retinal Fundus

Retinal photomontages, which are constructed by aligning and integrating multiple fundus images, are useful in diagnosing retinal diseases affecting peripheral retina. We present a novel framework for constructing retinal photomontages that fully leverage recent deep learning methods. Deep learning based object detection is used to define the order of image registration and blending. Deep learning based vessel segmentation is used to enhance image texture to improve registration performance within a two step image registration framework comprising rigid and non-rigid registration. Experimental evaluation demonstrates the robustness of our montage construction method with an increased amount of successfully integrated images as well as reduction of image artifacts.

DC-Gnet for detection of glaucoma in retinal fundus imaging

2020 ◽  
Vol 31 (5) ◽  
Author(s):  
Mamta Juneja ◽  
Sarthak Thakur ◽  
Anuj Wani ◽  
Archit Uniyal ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Fundus Imaging ◽  
Retinal Fundus

scholarly journals A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study (Preprint)

2021 ◽  
Author(s):  
Eugene Yu-Chuan Kang ◽  
Ling Yeung ◽  
Yi-Lun Lee ◽  
Cheng-Hsiu Wu ◽  
Shu-Yen Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multimodal Imaging ◽  
Vascular Diseases ◽  
Learning Model ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Retinal Diseases ◽  
Ophthalmic Imaging ◽  
Retinal Fundus ◽  
Deep Learning Model

BACKGROUND Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. OBJECTIVE The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. METHODS This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. RESULTS A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. CONCLUSIONS Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.

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