BackgroundWe modified and reconstructed a high image quality portable non-mydriatic fundus camera and compared it with the tabletop fundus camera to evaluate the efficacy of the new camera in detecting retinal diseases.MethodsWe designed and built a novel portable handheld fundus camera with telemedicine system. The image quality of fundus cameras was compared to that of existing commercial tabletop cameras by taking photographs of 364 eyes from the 254 patients. In all 800 fundus images taken by two camera types, 400 images per camera, were graded with the four image clarity classifications.ResultsUsing the portable fundus camera, 63% (252/400) images were graded as excellent overall quality, 20.5% (82/400) were good, 11.75% (47/400) were fair, and 4.75% (19/400) were inadequate. Using the tabletop fundus camera, 70.75% (283/400) images were graded as excellent overall quality, 20.4% (51/400) were good, 13.25% (53/400) were fair, and 3.25% (13/400) were inadequate. Common retinal diseases were easily identified from fundus images obtained from the portable fundus camera.ConclusionThe new type of non-mydriatic portable fundus camera was qualified to have professional quality of fundus images. The revolutionary screening camera provides a foundational platform which can potentially improve the accessibility of retinal screening programmes.

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Simultaneous IFVA and ICGA are generally safe procedures with an acceptable incidence of an adverse reaction. However, patients with drug allergy history may have a higher incidence and greater severity of an adverse reaction.

Background/aimsFundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification.MethodsA total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail.ResultsCompared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%–93.34% for prediagnosis assessment and an accuracy of 63.67%–88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement.ConclusionThis is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.

PurposeTo investigate the clinical features and cytochrome P450 family 4 subfamily V polypeptide 2 (CYP4V2) gene mutations in 14 Chinese families with Bietti crystalline dystrophy (BCD).MethodsSeventeen patients from 14 unrelated Chinese families with BCD were recruited for complete clinical ophthalmic examination and genetic study. The 11 exons of CYP4V2 were amplified from genomic DNA of all patients and their family members by polymerase chain reaction (PCR) and then sequenced. Exons of TIMP3 were also sequenced in BCD patient associated with choroidal neovascularization (CNV). One hundred and seventy unrelated healthy Chinese subjects were screened for mutations in CYP4V2.ResultsAll 17 patients with BCD had mutations in CYP4V2; one of these mutations was novel (c.219T>A, p.F73L) and four other mutations had been reported. The p.F73L mutation was a commonly detected mutation in our study (seven out of 34 alleles), either in the homozygous state or in the heterozygous state. Among the patients, considerable phenotypic variability was detected, both within and between families. Screening of TIMP3 did not find any mutation in the BCD patient associated with CNV.ConclusionThe novel CYP4V2 c.219T>A (p.F73L) mutation may be another recurrent mutation in Chinese patients with BCD. Our study expands the mutation spectrum of CYP4V2 and characterizes novel genotype–phenotype associations in Chinese patients with BCD.

Purpose: To establish quantitative profile of the morphologic changes among patients with active myopic choroidal neovascularization (mCNV) before and after anti-vascular endothelial growth factor (VEGF) therapy using optical coherence tomography angiography (OCTA) to assess the therapeutic response.Methods: Patients with active mCNV who received anti-VEGF injections between February 2017 to October 2020 and fit the study criteria were retrospectively reviewed. Quantitative analysis of their OCTA images were carried out to evaluate the morphologic features and vascular changes of mCNV lesions in response to anti-VEGF therapy. For further quantitative profiling, mCNV area, fractal dimension, vessel area, vessel density, vessel diameter, vessel length, vessel junction, junction density, and vessel tortuosity were obtained by means of advanced skeletonization postprocessing analyses.Results: Thirty-one eyes of 29 consecutive patients with OCTA-positive mCNV lesions (mean spherical equivalent: −12.55 ± 3.24 diopters) were included. The 31 cases were divided into two phenotypes at baseline: organized interlacing pattern (83.87%) and disorganized vascular loops pattern (16.13%). The values of mCNV area, fractal dimension, vessel area, vessel length, vessel junction, and junction density decreased remarkably 1 month after the initial anti-VEGF injection (p < 0.001). Although, vessel density, vessel diameter, and vessel tortuosity increased meanwhile, only vessel diameter displayed statistical significance (p = 0.027). Of note, relative ratio analysis showed that vessel junction was the most sensitive biomarker in response to anti-VEGF therapy, reflecting a mean decrease of 50.36%. Sensitivity lowered successively in biomarkers of vessel length, vessel area, junction density, mCNV area, and fractal dimension. In addition, percent change of mCNV area (r = 0.552, p = 0.002), fractal dimension (r = 0.446, p = 0.017), vessel area (r = 0.518, p = 0.005), and vessel length (r = 0.440, p = 0.019) were moderately associated with that of central retinal thickness.Conclusions: The study showed morphological as well as quantitative changes on OCTA responding to anti-VEGF treatment in mCNV patients, among which vessel junctions might be the most predictive biomarker. OCTA-based analysis, providing intuitive images and a large spectrum of quantitative data at the same time, could promote new insights into the therapeutic response assessment in mCNV patients.

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.

Idiopathic choroidal neovascularization (ICNV) is a disorder that primarily affecting patients younger than 50 years and can cause severe loss of vision. Choroidal abnormalities, especially choroidal inflammation, have been thought to be involved in the pathophysiology of ICNV. However, the exact pathogenesis of ICNV remains unclear. The aim of our study was investigate the levels of 27 inflammatory cytokines in the aqueous humor of eyes with ICNV, and to determine the effect of intravitreal injection of ranibizumab (IVR) on cytokine levels. Significantly higher levels of IL-2, IL-10, IL-15, IL-17, basic FGF, and GM-CSF were observed in patients with ICNV compared with controls. However, only IL-17 levels were significantly higher in patients with ICNV compared with controls after adjusting for axial length. Furthermore, there were significant correlations between the levels of IL-10, IL-17, GM-CSF, and VEGF and the lesion area. Significant changes in visual acuity and central retinal thickness were observed after IVR. Besides VEGF, IVR also significantly reduced the levels of IL-2, IL-10, basic FGF, and IL-12, however, the IL-6 levels were significantly increased. Our results suggest that there may be an involvement of IL-17-related inflammatory processes in the etiology of ICNV.