Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.
Algorithms ; Diabetic Retinopathy/diagnostic imaging* ; Fundus Oculi ; Humans ; Microaneurysm/diagnostic imaging*

Objective: We determined the diagnostic accuracies of the mydriatic, monoscopic, iPhone 6s+ optic nerve photographs with a 20D lens and the Melbourne Rapid Fields (MRF) visual fields iPad application. Methods: This was a prospective, cross-sectional, single-center study involving 47 non-glaucomatous and 49 glaucomatous eyes. Each eye underwent 2 visual field tests: MRF iPad application and the Humphrey Field Analyzer (HRF). Mydriatic photographs of the fundus were taken with two devices: an iPhone 6s+ combined with a 20 D lens and the Visucam 500 fundus camera. All printouts were evaluated by 2 independent, masked glaucoma specialists. Diagnostic accuracies between the modalities were computed. Agreements between different parameters of both devices were analyzed using Cohen’s kappa test. Results: Smartphone-based (iPhone 6s+) fundus photos had an overall sensitivity of 100%, specificity of 89.36%, positive predictive value (PPV) of 89.36% and negative predictive value (NPV) of 100%, with all kappa values between graders of each parameter above 0.61. Tablet-based Melbourne Rapid Fields test had a sensitivity of 81.82%, specificity of 86.54%, PPV of 83.72% and NPV of 84.91%, showing good agreement with the HRF with a kappa value of 0.68 ± 0.07. Conclusion Smartphone-based fundus photography and tablet-based visual field tests are comparable to the standard fundus photos and visual field tests in evaluating the optic nerve and visual field. These portable devices are reliable and appropriate tools for diagnosing glaucoma and can be used for documentation and testing in remote areas and in a wider range of settings.
Fundus Oculi

Objective: To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk factors. Methods: Subjects aged>18 years with complete clinical examination data from 149 hospitals and medical examination centers in China were included in this retrospective study. Two radiologists, who were not aware of the study design, independently evaluated the coronary angiography images of each subject to make CHD diagnosis. A deep learning model using convolutional neural networks (CNN) was used to label the fundus images according to the presence or absence of CHD, and the model was proportionally divided into training and test sets for model training. The prediction performance of the model was evaluated in the test set using monocular and binocular fundus images respectively. Prediction efficacy of the algorithm for cardiovascular risk factors (e.g., age, systolic blood pressure, gender) and coronary events were evaluated by regression analysis using the area under the receiver operating characteristic curve (AUC) and R² correlation coefficient. Results: The study retrospectively collected 51 765 fundus images from 25 222 subjects, including 10 255 patients with CHD, and there were 14 419 male subjects in this cohort. Of these, 46 603 fundus images from 22 701 subjects were included in the training set and 5 162 fundus images from 2 521 subjects were included in the test set. In the test set, the deep learning model could accurately predict patients' age with an R² value of 0.931 (95%CI 0.929-0.933) for monocular photos and 0.938 (95%CI 0.936-0.940) for binocular photos. The AUC values for sex identification from single eye and binocular retinal fundus images were 0.983 (95%CI 0.982-0.984) and 0.988 (95%CI 0.987-0.989), respectively. The AUC value of the model was 0.876 (95%CI 0.874-0.877) with either monocular fundus photographs and AUC value was 0.885 (95%CI 0.884-0.888) with binocular fundus photographs to predict CHD, the sensitivity of the model was 0.894 and specificity was 0.755 with accuracy of 0.714 using binocular fundus photographs for the prediction of CHD. Conclusion: The deep learning model based on fundus photographs performs well in identifying coronary heart disease and assessing related risk factors such as age and sex.
Humans ; Male ; Retrospective Studies ; Deep Learning ; Fundus Oculi ; ROC Curve ; Algorithms ; Risk Factors ; Coronary Disease/diagnostic imaging*

The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.
Algorithms ; Fundus Oculi ; Optic Disk ; ROC Curve ; Retinal Vessels/diagnostic imaging*

Glaucoma is the leading cause of irreversible blindness, but its early symptoms are not obvious and are easily overlooked, so early screening for glaucoma is particularly important. The cup to disc ratio is an important indicator for clinical glaucoma screening, and accurate segmentation of the optic cup and disc is the key to calculating the cup to disc ratio. In this paper, a full convolutional neural network with residual multi-scale convolution module was proposed for the optic cup and disc segmentation. First, the fundus image was contrast enhanced and polar transformation was introduced. Subsequently, W-Net was used as the backbone network, which replaced the standard convolution unit with the residual multi-scale full convolution module, the input port was added to the image pyramid to construct the multi-scale input, and the side output layer was used as the early classifier to generate the local prediction output. Finally, a new multi-tag loss function was proposed to guide network segmentation. The mean intersection over union of the optic cup and disc segmentation in the REFUGE dataset was 0.904 0 and 0.955 3 respectively, and the overlapping error was 0.178 0 and 0.066 5 respectively. The results show that this method not only realizes the joint segmentation of cup and disc, but also improves the segmentation accuracy effectively, which could be helpful for the promotion of large-scale early glaucoma screening.
Diagnostic Techniques, Ophthalmological ; Fundus Oculi ; Glaucoma/diagnostic imaging* ; Humans ; Neural Networks, Computer ; Optic Disk/diagnostic imaging*

Based on the Gullstrand I model eye, a simplified model eye for testing fundus imaging device is designed. The model eye can reach the following requirements:(1) The refractive characteristics of the ocular refractive tissue are simulated, and the equivalent focal length in air is 17 mm; (2) The differences between relative refractive index differences of the adjacent materials of the simplified model eye and relative refractive index differences of any adjacent two layers (cornea and aqueous humor, aqueous humor and lens, lens and vitreous body) of the Gullstrand I model eye are not more than 1%; (3) In the case of the incident aperture diameter of 3 mm, the differences of radii of the diffuse spots formed by the paraxial light and the axial light are not more than 15%; (4) The differences of angles of chief ray and tangent line of the fundus are not more than 1°; (5) In the case of the incident aperture diameter of 3 mm, the differences of MTF values of the near axis light are not more than 0.1. The simplified model eye can be expected to be used for testing fundus imaging device instead of the test method in ISO 10940:2009 Ophthalmic instruments-Fundus cameras.
Cornea ; Fundus Oculi ; Lens, Crystalline ; diagnostic imaging ; Refraction, Ocular

PURPOSE: To investigate the effects of cataract grade based on wide-field fundus imaging on macular thickness measured by spectral domain optical coherence tomography (SD-OCT) and its signal-to-noise ratio (SNR). METHODS: Two hundred cataract patients (200 eyes) with preoperative measurements by wide-field fundus imaging and macular SD-OCT were enrolled. Cataract severity was graded from 1 to 4 according to the degree of macular obscuring by cataract artifact in fundus photo images. Cataract grade based on wide-field fundus image, the Lens Opacity Classification System III, macular thickness, and SD-OCT SNR were compared. All SD-OCT B-scan images were evaluated to detect errors in retinal layer segmentation. RESULTS: Cataract grade based on wide-field fundus imaging was positively correlated with grade of posterior subcapsular cataracts (rho = 0.486, p < 0.001), but not with nuclear opalescence or cortical cataract using the Lens Opacity Classification System III. Cataract grade was negatively correlated with total macular thickness (rho = −0.509, p < 0.001) and SD-OCT SNR (rho = −0.568, p < 0.001). SD-OCT SNR was positively correlated with total macular thickness (rho = 0.571, p < 0.001). Of 200 eyes, 97 (48.5%) had segmentation errors on SD-OCT. As cataract grade increased and SD-OCT SNR decreased, the percentage of eyes with segmentation errors on SD-OCT increased. All measurements of macular thickness in eyes without segmentation errors were significantly greater than those of eyes with segmentation errors. CONCLUSIONS: Posterior subcapsular cataracts had profound effects on cataract grade based on wide-field fundus imaging. As cataract grade based on wide-field fundus image increased, macular thickness tended to be underestimated due to segmentation errors in SD-OCT images. Segmentation errors in SD-OCT should be considered when evaluating macular thickness in eyes with cataracts.
Artifacts ; Cataract* ; Classification ; Fundus Oculi ; Humans ; Iridescence ; Retinaldehyde ; Signal-To-Noise Ratio ; Tomography, Optical Coherence

BACKGROUNDA sensitive method is required to detect retinal hamartomas in patients with tuberous sclerosis complex (TSC). The aim of the present study was to compare the color fundus photography, infrared imaging (IFG), and optical coherence tomography (OCT) in the detection rate of retinal hamartoma in patients with TSC.

METHODSThis study included 11 patients (22 eyes) with TSC, who underwent color fundus photography, IFG, and spectral-domain OCT to detect retinal hamartomas. TSC1 and TSC2RESULTS: The mean age of the 11 patients was 8.0 ± 2.1 years. The mean spherical equivalent was -0.55 ± 1.42 D by autorefraction with cycloplegia. In 11 patients (22 eyes), OCT, infrared fundus photography, and color fundus photography revealed 26, 18, and 9 hamartomas, respectively. The predominant hamartoma was type I (55.6%). All the hamartomas that detected by color fundus photography or IFG can be detected by OCT.

CONCLUSIONAmong the methods of color fundus photography, IFG, and OCT, the OCT has higher detection rate for retinal hamartoma in TSC patients; therefore, OCT might be promising for the clinical diagnosis of TSC.

Adolescent ; Child ; Diagnostic Techniques, Ophthalmological ; Eye Diseases ; diagnosis ; Female ; Fundus Oculi ; Hamartoma ; diagnosis ; Humans ; Male ; Photography ; methods ; Tomography, Optical Coherence ; methods ; Tuberous Sclerosis ; diagnosis

No abstract available.
Aged ; Diagnosis, Differential ; Female ; Fluorescein Angiography ; Fundus Oculi ; Humans ; *Intraocular Pressure ; Regional Blood Flow/*physiology ; Retina/*diagnostic imaging ; Retinal Hemorrhage/*diagnosis/physiopathology ; Retinal Vein Occlusion/*diagnosis

PURPOSE: We evaluated fundus and fluorescein angiography (FAG) findings and characteristics that can help distinguish nonarteritic anterior ischemic optic neuropathy (NAION) from optic neuritis (ON). METHODS: Twenty-three NAION patients and 17 ON with disc swelling patients were enrolled in this study. We performed fundus photography and FAG. The disc-swelling pattern, hyperemia grade, presence of splinter hemorrhages, cotton-wool spots, artery/vein ratio and degree of focal telangiectasia were investigated. The FAG findings for each patient were compared with respect to the following features: the pattern of disc leakage in the early phase, arteriovenous (artery/vein) transit time (second), and the presence and pattern of the filling delay. RESULTS: Cotton-wool spots, focal telangiectasia, and venous congestion were more common in the affected eyes of NAION patients. Upon FAG, 76.5% of the patients in the ON group exhibited normal choroidal circulation. However, 56.5% of patients in the NAION group demonstrated abnormal filling defects, such as peripapillary, generalized, or watershed zone filling delays. CONCLUSIONS: Fundus findings, including cotton-wool spots, focal telangiectasia, and venous congestion in the affected eye, may be clues that can be used to diagnose NAION. In addition, choroidal insufficiencies on FAG could be also helpful in differentiating NAION from ON.
Choroid/blood supply/*diagnostic imaging ; Female ; Fluorescein Angiography/*methods ; Fundus Oculi ; Humans ; Male ; Middle Aged ; Optic Disk/blood supply/*diagnostic imaging ; Optic Neuritis/*diagnosis ; Optic Neuropathy, Ischemic/*diagnosis ; Photography/*methods ; Retrospective Studies