Spatial distribution of diabetic capillary non‐perfusion

Falavarjani, Khalil Ghasemi; Mirshahi, Reza; Riazi-Esfahani, Hamid; Anvari, Pasha; Habibi, Abbas; Khorasani, Maryam; Shad, Esrafil

doi:10.1111/micc.12719

Cited by 8 publications

(6 citation statements)

References 28 publications

(30 reference statements)

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“…In a previous study 29 , we showed that the extent of non-perfusion is higher in SCP than DCP in eyes with DR without DME. Similarly, current study shows that the non-perfusion area was more extensive in SCP.…”

Section: Discussionmentioning

confidence: 67%

“…Also, we did not assess structural changes in OCT b scans as it is shown that the inner retinal layers disorganization and paracentral acute middle maculopathy are related to macular ischemia in different levels of capillary plexuses 34 . Despite recent advances in OCTA software, many of artifacts cannot be readily fixed with present technologies 29 . Although low-quality images were excluded from this study and segmentation error was manually corrected, other forms of artifacts, especially due to cysts could have affected our measurements.…”

Section: Discussionmentioning

confidence: 99%

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Differentiating features of OCT angiography in diabetic macular edema

Mirshahi

Riazi-Esfahani

Pour

et al. 2021

Sci Rep

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The purpose of current study was to evaluate different optical coherence tomography angiography (OCTA) metrics in eyes with diabetic retinopathy with and without diabetic macular edema (DME). In this retrospective study, macular OCTA images of eyes with non-proliferative or proliferative diabetic retinopathy were evaluated. Vascular density, vascular complexity and non-perfusion densities were compared between eyes with and without DME. One-hundred-thirty-eight eyes of 92 diabetic patients including 49 eyes with DME were included. In multivariate analysis, the presence of DME was positively associated with geometric perfusion deficit (GPD) in superficial capillary plexus (SCP), capillary non-perfusion (CNP) of SCP, and GPD in deep capillary plexus (DCP) (all P < 0.05). In eyes with DME, central foveal thickness was associated with VD ratio (SCP/DCP) (P = 0.001) and FAZ area (P = 0.001). In conclusion, in eyes with diabetic retinopathy, the presence of DME was associated with more extensive capillary non-perfusion compared to those with no macular edema.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Differentiating features of OCT angiography in diabetic macular edema

Mirshahi

Riazi-Esfahani

Pour

et al. 2021

Sci Rep

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“…4,5 For example, DR is a common complication of both types 1 and 2 diabetes mellitus (T1DM and T2DM) that stems from high blood sugar levels, which damage small retinal blood vessels. [6][7][8] DR is the leading cause of blindness in the United States for individuals between the ages of 20 and 64. 9,10 Due to the high prevalence of T1DM and T2DM worldwide, DR incidence is expected to increase over the next 20 years.…”

Section: Introductionmentioning

confidence: 99%

Statistical and topological summaries aid disease detection for segmented retinal vascular images

2023

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Objective Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in‐person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. Methods We compute 13 separate descriptor vectors (5 statistical, 8 topological) to summarize the morphology of retinal vessel segmentation images and train support vector machines to predict each image's disease classification from the summary vectors. We assess the performance of each descriptor vector, using five‐fold cross validation to estimate their accuracy. We apply these methods to four datasets that were assembled from four existing data repositories; three datasets contain segmented retinal vascular images from one of the repositories, whereas the fourth “All” dataset combines images from four repositories. Results Among the 13 total descriptor vectors considered, either a statistical Box‐counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels. On the combined “All” dataset, the Box‐counting vector outperforms all other descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets. Conclusion Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease and assessing their current limitations. These methods could be incorporated into automated disease assessment tools.

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“…Retinal non-perfusion area (NPA) is a vision-threatening condition that is strongly associated with retinal neovascularization (NV) ( 1 ), vitreous hemorrhage, and macular edema in retinal diseases, such as diabetic retinopathy (DR) ( 2 ) and branch retinal vein occlusion (BRVO) ( 3 , 4 ). In ophthalmological practice, a standard and early location of NPA is critical for clinical decision-making, such as prompt laser photocoagulation ( 2 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion

Miao

Zou

et al. 2022

Front. Med.

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PurposeTo develop artificial intelligence (AI)-based deep learning (DL) models for automatically detecting the ischemia type and the non-perfusion area (NPA) from color fundus photographs (CFPs) of patients with branch retinal vein occlusion (BRVO).MethodsThis was a retrospective analysis of 274 CFPs from patients diagnosed with BRVO. All DL models were trained using a deep convolutional neural network (CNN) based on 45 degree CFPs covering the fovea and the optic disk. We first trained a DL algorithm to identify BRVO patients with or without the necessity of retinal photocoagulation from 219 CFPs and validated the algorithm on 55 CFPs. Next, we trained another DL algorithm to segment NPA from 104 CFPs and validated it on 29 CFPs, in which the NPA was manually delineated by 3 experienced ophthalmologists according to fundus fluorescein angiography. Both DL models have been cross-validated 5-fold. The recall, precision, accuracy, and area under the curve (AUC) were used to evaluate the DL models in comparison with three types of independent ophthalmologists of different seniority.ResultsIn the first DL model, the recall, precision, accuracy, and area under the curve (AUC) were 0.75 ± 0.08, 0.80 ± 0.07, 0.79 ± 0.02, and 0.82 ± 0.03, respectively, for predicting the necessity of laser photocoagulation for BRVO CFPs. The second DL model was able to segment NPA in CFPs of BRVO with an AUC of 0.96 ± 0.02. The recall, precision, and accuracy for segmenting NPA was 0.74 ± 0.05, 0.87 ± 0.02, and 0.89 ± 0.02, respectively. The performance of the second DL model was nearly comparable with the senior doctors and significantly better than the residents.ConclusionThese results indicate that the DL models can directly identify and segment retinal NPA from the CFPs of patients with BRVO, which can further guide laser photocoagulation. Further research is needed to identify NPA of the peripheral retina in BRVO, or other diseases, such as diabetic retinopathy.

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Spatial distribution of diabetic capillary non‐perfusion

Cited by 8 publications

References 28 publications

Differentiating features of OCT angiography in diabetic macular edema

Differentiating features of OCT angiography in diabetic macular edema

Statistical and topological summaries aid disease detection for segmented retinal vascular images

Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion

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