scholarly journals The influence of delayed treatment due to COVID-19 on patients with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy

2021 ◽  
Vol 12 ◽  
pp. 204062232110263
Author(s):  
Xinyu Zhao ◽  
Lihui Meng ◽  
Mingyue Luo ◽  
Weihong Yu ◽  
Hanyi Min ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Visual Function ◽  
Polypoidal Choroidal Vasculopathy ◽  
Age Related Macular Degeneration ◽  
College Hospital ◽  
Delayed Treatment ◽  
Medical College ◽  
Age Related ◽  
The Impact ◽  
Choroidal Vasculopathy

Purpose: To explore the impact of coronavirus disease 2019 (COVID-19) on the prognosis of patients with neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV), and share the experience in managing them during pandemics. Method: This is a retrospective study of nAMD and PCV patients treated at Peking Union Medical College Hospital from 31 December 2019 to 1 August 2020. Baseline demographic and clinical characteristics, best corrected visual acuity (BCVA), optical coherence tomography (OCT) features, duration of delayed treatment and number of anti-vascular endothelial growth factor (VEGF) injections were analyzed. Results: A total of 130 nAMD patients (155 eyes) and 76 PCV patients (89 eyes) were identified. Compared to the conditions before COVID-19, the BCVA of delayed cases decreased significantly, and the proportion of patients presenting with sub-macular scar was significantly greater in the delayed treatment group ( p < 0.05). The BCVA of non-delayed cases remained stable, with the percentage of patients with disease activity sub-retinal fluid and hemorrhage at the fovea decreasing significantly ( p < 0.05). The stable cases who did not require anti-VEGF treatment had significantly worse baseline and final BCVA, these patients were likely to be chronic and ‘burnt out’ cases with significantly worse anatomical structures ( p < 0.05). Conclusions: The delayed cases due to the pandemic suffered compromised visual function and a higher rate of sub-macular scar formation, while the visual function of non-delayed cases remained stable with favorable anatomical outcomes, suggesting the importance of regular follow-up for nAMD and PCV patients. Besides, effective measures of hospitals during pandemics are crucial to provide timely treatment for chronic disease.

Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks

2020 ◽  
pp. bjophthalmol-2020-315817 ◽  
Author(s):  
Zhiyan Xu ◽  
Weisen Wang ◽  
Jingyuan Yang ◽  
Jianchun Zhao ◽  
Dayong Ding ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Gold Standard ◽  
Polypoidal Choroidal Vasculopathy ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Deep Convolutional Neural Networks ◽  
College Hospital ◽  
Data Set ◽  
Age Related ◽  
Choroidal Vasculopathy

AimsTo investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images.MethodsA retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset.ResultsOn a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well.ConclusionA bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.

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