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Polypoidal choroidal vasculopathy (PCV) is a common choroidal vascular disease particularly in Asians. However, the underlying pathogenesis of PCV is still yet to be fully elucidated, and the correlation between choroidal vasculature and treatment response of PCV are poorly understood. Accordingly, we sought to find clues to understand the pathogenesis and prognosis of PCV by quantitatively evaluating choroidal vasculature from the entire fundus using ultra-widefield (UWF) indocyanine green angiography (ICGA). In this study, 32 eyes from 29 patients with treatment naïve PCV and 30 eyes from 30 healthy control participants were enrolled. Choroidal vascular density (CVD) of PCV eyes was higher than normal eyes in majority regions including the periphery. CVD was positively correlated with choroidal thickness and choroidal hyperpermeability, supporting that the pathogenesis of PCV may include choroidal congestion and dilatation. Thicker choroid and higher CVD were also correlated with poor treatment response after anti-VEGF injections. The CVD, quantified from UWF ICGA can also be used as an effective image biomarker to predict the treatment response in PCV.

As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classification algorithm to diagnose DR and identify referable status using optical coherence tomography angiography (OCTA) images with convolutional neural network (CNN) model and verified its feasibility by comparing its performance with that of conventional machine learning model. Ground truths for classifications were made based on ultra-widefield fluorescein angiography to increase the accuracy of data annotation. The proposed CNN classifier achieved an accuracy of 91–98%, a sensitivity of 86–97%, a specificity of 94–99%, and an area under the curve of 0.919–0.976. In the external validation, overall similar performances were also achieved. The results were similar regardless of the size and depth of the OCTA images, indicating that DR could be satisfactorily classified even with images comprising narrow area of the macular region and a single image slab of retina. The CNN-based classification using OCTA is expected to create a novel diagnostic workflow for DR detection and referral.

Introduction: Choroidal osteoma (CO) is a rare benign tumor that particularly affects young, healthy women. Its prognosis is influenced by complications, such as choroidal neovascularization (CNV), subretinal hemorrhage, subretinal fluid (SF), decalcification status, and overlying retinal pigment epithelium (RPE) atrophy. In case of CNV as the complication of CO, it is typically present in the classic form; however, reports on polypoidal choroidal vasculopathy (PCV) have been rare. Here, we report a case of an older, male patient with PCV as a complication of CO. Patient concerns: A 70-year-old male patient visited the hospital with vision impairment in the right eye since 2 weeks. Diagnosis: Fundus examination revealed a red-yellow, well-demarcated, scalloped lesion around the optic nerve in each eye; the lesions were highly reflective on ultrasound examination, and thus, CO was diagnosed. Indocyanine green fluorescence angiography and optical coherence tomography (OCT) revealed that the right eye also had PCV accompanied with SF. OCT confirmed the presence of large quiescent type 1 CNV bilaterally in decalcified areas of the lesions adjacent to the optic nerve. Interventions: Intravitreal bevacizumab (IB) injection was performed. Outcomes: Best-corrected visual acuity had improved and OCT showed a decrease in the SF, while OCT angiography showed partial regression of branching vascular network. Conclusion: CO can be accompanied by quiescent type 1 CNV; this should be closely monitored because it can progress to PCV. Optical coherence tomography, alongside indocyanine green fluorescence angiography, is useful for the diagnosis and monitoring of potential CNV as a complication of CO.

To develop an automated diabetic retinopathy (DR) staging system using optical coherence tomography angiography (OCTA) images with a convolutional neural network (CNN) and to verify the feasibility of the system.In this retrospective cross-sectional study, a total of 918 data sets of 3 × 3 mm2 OCTA images and 917 data sets of 6 × 6 mm2 OCTA images were obtained from 1118 eyes. A deep CNN and four traditional machine learning models were trained with annotations made by a retinal specialist based on ultra-widefield fluorescein angiography. Separately, the same images of the test data sets were independently graded by two human experts. The results of the CNN algorithm were compared with those of traditional machine learning-based classifiers and human experts.The proposed CNN achieved an accuracy of 0.728, a sensitivity of 0.675, a specificity of 0.944, an F1 score of 0.683, and a quadratic weighted κ of 0.908 for a six-level staging task, which were far superior to the results of traditional machine learning methods or human experts. The CNN algorithm showed a better performance using 6 × 6 mm2 rather than 3 × 3 mm2 sized OCTA images and using combined data rather than a separate OCTA layer alone.CNN-based classification using OCTA images can provide reliable assistance to clinicians for DR classification.This CNN algorithm can guide the clinical decision for invasive angiography or referrals to ophthalmology specialists, helping to create more efficient diagnostic workflow in primary care settings.