Ocular Fundus Images Research Articles

Glaucoma detection: Binocular approach and clinical data in machine learning.

In this work, we present a multi-modal machine learning method to automate early glaucoma diagnosis. The proposed methodology introduces two novel aspects for automated diagnosis not previously explored in the literature: simultaneous use of ocular fundus images from both eyes and integration with the patient's additional clinical data. We begin by establishing a baseline, termed monocular mode, which adheres to the traditional approach of considering the data from each eye as a separate instance. We then explore the binocular mode, investigating how combining information from both eyes of the same patient can enhance glaucoma diagnosis accuracy. This exploration employs the PAPILA dataset, comprising information from both eyes, clinical data, ocular fundus images, and expert segmentation of these images. Additionally, we compare two image-derived data modalities: direct ocular fundus images and morphological data from manual expert segmentation. Our method integrates Gradient-Boosted Decision Trees (GBDT) and Convolutional Neural Networks (CNN), specifically focusing on the MobileNet, VGG16, ResNet-50, and Inception models. SHAP values are used to interpret GBDT models, while the Deep Explainer method is applied in conjunction with SHAP to analyze the outputs of convolutional-based models. Our findings show the viability of considering both eyes, which improves the model performance. The binocular approach, incorporating information from morphological and clinical data yielded an AUC of 0.796 (±0.003 at a 95% confidence interval), while the CNN, using the same approach (both eyes), achieved an AUC of 0.764 (±0.005 at a 95% confidence interval).

Smartphone Aided Funduscopy in Albino Rabbits

Aims: The methodology, utility and clinical feasibility of using a smartphone camera to obtain images of ocular fundus of albino rabbits with ocular hypertension and normotension were studied. A comparative analysis of fundus images obtained from two popularly used smartphone models, android and iOS device was performed on 36 adult albino rabbits. Study Design: A cross-sectional study involving two cohorts of ocular hypertensive and normotensive albino rabbits. Place and Duration of Study: Department of Veterinary Surgery and Radiology, College of Veterinary and Animal Sciences, Mannuthy for an eight-week period (2023). Methodology: A total of 36 adult albino rabbits (18 males and 18 females; age range 6- 8 months of age) with ocular normotensive eyes and unilateral ocular hypertension-induced models were used. The conscious animals were gently restrained in an acrylic restrainer. Instillation of topical mydriatic solution (tropicamide 0.8% and phenylephrine 5%) facilitated visual examination and enhanced field of view along with auxiliary condensing lenses of 20 and 40 Diopters. A comparison of fundus images obtained from two popularly used smartphone models, android and iOS devices was performed on 36 adult albino rabbits. Results: Fundus images comprising the optic nerve head and peripapillary retina were obtained with adequate spatial resolution and detail. The variation between optic disc of ocular hypertensive eye revealed mild edema and cupping of optic disc. Normal merangiotic retina and optic disc were clearly obtained in normotensive rabbit eyes. The iOS device used in this case yielded better images in resolution and field of view compared to the android version used in this case. Conclusion: Smart phone funduscopy offered good detail in contrast and resolution even in amelanotic eyes of albino rabbits using inbuilt technology without the need for an external adapter or sophisticated additional software. It is an animal-friendly futuristic solution for digital documentation, telemedicine, teaching and clinical prognosis formulation.

Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent.

While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces a novel optimizer that integrates the Caputo fractional gradient descent (CFGD) method with the cuckoo search algorithm (CSA) to enhance accuracy and convergence speed, seeking optimal solutions. The proposed optimizer's performance is assessed by training well-known Vgg16, AlexNet, and GoogLeNet models on 400 fundus images, equally divided between benign and malignant classes. Results demonstrate the significant potential of the proposed optimizer in improving classification accuracy and convergence speed. In particular, the mean accuracy attained by the proposed optimizer is 86.43%, 87.42%, and 87.62% for the Vgg16, AlexNet, and GoogLeNet models, respectively. The performance of our optimizer is compared with existing approaches, namely stochastic gradient descent with momentum (SGDM), adaptive momentum estimation (ADAM), the original cuckoo search algorithm (CSA), Caputo fractional gradient descent (CFGD), beetle antenna search with ADAM (BASADAM), and CSA with ADAM (CSA-ADAM). Evaluation criteria encompass accuracy, robustness, consistency, and convergence speed. Comparative results highlight significant enhancements across all metrics, showcasing the potential of deep learning techniques with the proposed optimizer for accurately identifying ocular tumors. This research contributes significantly to the development of computer-aided diagnosis systems for ocular tumors, emphasizing the benefits of the proposed optimizer in medical image classification domains.

Evaluating the accuracy of the Ophthalmologist Robot for multiple blindness-causing eye diseases: a multicentre, prospective study protocol

IntroductionEarly eye screening and treatment can reduce the incidence of blindness by detecting and addressing eye diseases at an early stage. The Ophthalmologist Robot is an automated device that can...

A study of retinal vascular patterns in 200 subjects

Every eye has its own totally unique pattern of blood vessels and the vascular system in human retina has a unique property as it is easily observed in its natural living state. The characteristic radiating pattern of retinal blood vessels in modern era plays a key role in biometrics (for identification) and medical diagnosis. Retinal blood vessel pattern is a highly stable pattern and one of the hardest biometric to forge. This property makes it one of the best biometric characteristics in high security environments. Thus, ocular fundus images can provide valuable information on pathological changes and it is also used in screening of individuals. Analysis and interpretation of fundus images not only has become a necessary and important diagnostic tool in ophthalmology but also in personal identification in biometric. Therefore, the present study was designed to provide normative database for retinal vascular patterns in the study population. The present study was conducted on 200 subjects. Digital photographs of fundus of eyes were taken with fundus camera and the retinal vasculature was studied and analyzed. Results showed that branching of retinal vasculature was more extensive in temporal half of both eyes. Also, higher branching in superotemporal quadrant of both eyes were seen. This is the first attempt in providing normative database for retinal vascular pattern. However, further studies are needed and this data may provide a reference for future research.

Scanning laser ophthalmoscopy ‐ Basic optical principles

Scanning Laser Ophthalmoscopy (SLO) produces an ocular fundus image by moving a focused laser beam via scanning mirrors over the retina in a grid pattern and registering the reflected light from each scanned point. In confocal SLO, a pinhole is placed in front of the detector to cut off scattered or defocused light coming from outside the point of interest, which otherwise can blur the image. This results in a focused, high‐contrast image of a single tissue layer located at the focal plane. Tomographic information can be extracted by moving the plane of the pinhole. The use of various wavelengths allows for different applications, such as fluorescein angiography, indocyanine green angiography, and autofluorescence imaging. This lecture will present the basic principles of SLO, as well as discuss notable applications and variants of the technology.

Evidence-based guidelines for diagnosis and treatment of age-related macular degeneration in China (2023)

Age-related macular degeneration (AMD) is the main cause of low vision and even blindness in the elderly. With the aging of our population, the number of AMD patients will continue to rise. In the past decade, the rapid development of ocular fundus imaging technology has provided a new perspective and approach for the classification, diagnosis and follow-up of AMD. The advent of new drugs has provided more diverse intervention and treatment methods for AMD, especially for neovascular AMD, and the emphasis on accurate and individualized treatment has put forward higher requirements for retinal specialists. Therefore, based on the latest evidence-based medical information, combined with the international guidelines and the current situation of China's social and economic development, experts from the Chinese Vitreo-Retinal Society of Chinese Medical Association, the Fundus Disease Group of Chinese Ophthalmologist Association, and the National Clinical Research Center for Eye Diseases gave recommendations around eight clinical problems and formed China's guidelines for the clinical diagnosis and treatment of AMD. With the implementation of these guidelines, we can standardize the diagnosis, treatment, prevention and follow-up of AMD in China.

Fundus Imaging via Infrared Camera without Mydriatics in Holstein Calves

This study aimed to record the ocular fundus images of healthy Holstein calves under field conditions. The fundus of 34 eyes of healthy 17 Holstein calves was examined with a fundus camera, which does not require mydriatics, as it has been designed especially for animals and provides imaging with infrared light. The findings showed that the green-yellow tapetal zone was dominant in all calves, the optic disc was oval, and the number of primary arteries and veins originating from the center varied between 4 and 5. The vascular pattern was holangiotic. A remnant of the hyaloid artery (Bergmeister’s papilla) was detected as a gray dot in the middle of the disc. It was noted that the non-tapetal area was homogeneous, brown-black, and rich in choroidal vascular structure. Imaging the ocular fundus is essential in diagnosing some systemic and hereditary diseases in farm animals. However, herd-based ophthalmoscopic screening in farm animals is difficult under field conditions. By using this portable fundus camera, fundus examination can be performed easily under field conditions without taking the animals to the hospital. The standard ophthalmoscopic fundus images of healthy Holstein calves presented in this study will contribute to the literature.

Scanning laser ophthalmoscopy – basic optical principles

AbstractScanning Laser Ophthalmoscopy (SLO) produces an ocular fundus image by moving a focused laser beam via scanning mirrors over the retina in a grid pattern and registering the reflected light from each scanned point. In confocal SLO, a pinhole is placed in front of the detector to cut off scattered or defocused light coming from outside the point of interest, which otherwise can blur the image. This results in a focused, high‐contrast image of a single tissue layer located at the focal plane. Tomographic information can be extracted by moving the plane of the pinhole. The use of various wavelengths allows for different applications, such as fluorescein angiography, indocyanine green angiography, and autofluorescence imaging. This lecture will present the basic principles of SLO, as well as discuss notable applications and variants of the technology.

Comparative study on early recognition and identifying diabetic retinopathy with different layers in CNN

Diabetes is the most prevalent condition worldwide, and diabetic retinopathy (DR) is a subsequent condition caused by acute diabetic cases. It causes severe degeneration of the retina. The compounding blood vessels bloat and often burst, causing fluid leaks in the aqueous humor. This, in turn, causes the creation of undesirable nerve fiber infractions from the occlusion of arteries. Diagnosis requires a manual retinal examination that can often be inconsistent and deliberate with potential flaws in the diagnosis. Early detection through an ophthalmologist is paramount to prevent the prognosis of severe vision loss. Considering the current leap of machine learning in the field of healthcare, early detection of DR can be potentially made efficient with intelligent systems. This research proposes methodologies to fine-tune the existing pre-trained architectures, attaining the classification accuracies of 98% to classify the ocular fundus images which identify early prediction of diabetes. Additionally, this study presents an exposition of other equally scrutinized approaches to ultimately showcase a deep neural network architecture that can precisely classify normal fundus and degenerated fundus from the lowest to the most severe hierarchy. Among several layers in the CNN model pre-tuning and post-tuning exception layers outperformed with good results.

AMD-Net: Automatic subretinal fluid and hemorrhage segmentation for wet age-related macular degeneration in ocular fundus images

Age-related macular degeneration (AMD) is a chronic, progressively degenerative disorder of the macular. Wet AMD is responsible for 80 to 90 percent of all AMD-related blindness. Nowadays, ophthalmologists can diagnose wet AMD based on subretinal fluid and hemorrhage in ocular fundus images. However, this diagnosis process is time-consuming and influenced by subjective factors. Deep learning methods have achieved significant improvements for segmentation tasks in the retinal image, but only a few studies have focused on the subretinal fluid and hemorrhage lesion segmentation of wet AMD. This paper proposed AMD-Net, a novel U-Net architecture, which can segment the subretinal fluid and hemorrhage lesions of the wet AMD in ocular fundus images. The proposed AMD-Net consists of three components: encoder feature fusion unit (EFFU), skip connection block (SKB), and decoder attention block (DAB). The AMD-Net adopts the EFFU to extract and fuse the multi-scale features. And with the attention module, the EFFU can assign a higher weight for discriminative features. To reduce the semantic gap between the encoder features and decoder features, the AMD-Net designs an SKB. Furthermore, the AMD-Net introduces a DAB, a new module based on the UNet 3+ decoder, designed to leverage the local context information and enhance the contribution of high-level semantic features in tiny regions segmentation. We evaluate the proposed method on a private dataset collected by ourselves. Our model achieves 67.18% subretinal fluid Dice, 66.51% hemorrhage Dice, and 77.07% average Dice on this dataset. The experimental results indicate that our proposed AMD-Net is superior to the state-of-the-art deep learning methods.

Role of Prostaglandins in Nitric Oxide-Induced Glial Cell-Mediated Vasodilation in Rat Retina.

We previously identified that NO derived from neuronal cells acts on glial cells and causes vasodilation in the healthy rat retina via the release of epoxyeicosatrienoic acids (EETs) and prostaglandins (PGs) by activation of the arachidonic acid cascade. However, it is not clear which PG types are involved in these responses. The aim of the present study was to identify prostanoid receptors involved in glial cell-derived vasodilation induced by NO in rat retina. Male Wistar rats were used to examine the effects of intravitreal pretreatment with indomethacin, a cyclooxygenase inhibitor; PF-04418948, a prostanoid EP2 receptor antagonist; and CAY10441, a prostanoid IP receptor antagonist, on the changes in the retinal arteriolar diameter induced by intravitreal administration of NOR3, an NO donor. Retinal arteriolar diameters were measured using ocular fundus images captured with a high-resolution digital camera in vivo. The increase in the retinal arteriolar diameter induced by intravitreal injection of NOR3 was significantly suppressed by intravitreal pretreatment with indomethacin and PF-04418948, but not by CAY10441. The dose of PF-04418948 and CAY10441 injected intravitreally in the present study significantly reduced the increase in the retinal arteriolar diameter induced by prostaglandin E2 (PGE2) and prostaglandin I2 (PGI2), respectively. These results suggest that activation of the arachidonic acid cascade and subsequent stimulation of prostanoid EP2 receptors are involved in rat retinal vasodilatory responses evoked by NO-induced glial cell stimulation. Therefore, glial cell-derived PGE2, similar to EETs, may play an important role in retinal vasodilatory mechanisms.

Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017-2021.

PurposeThe purpose of this study was to investigate the hotspots and research trends of ophthalmology research.MethodOphthalmology research literature published between 2017 and 2021 was obtained in the Web of Science Core Collection database. The bibliometric analysis and network visualization were performed with the VOSviewer and CiteSpace. Publication-related information, including publication volume, citation counts, countries, journals, keywords, subject categories, and publication time, was analyzed.ResultsA total of 10,469 included ophthalmology publications had been cited a total of 7,995 times during the past 5 years. The top countries and journals for the number of publications were the United States and the Ophthalmology. The top 25 global high-impact documents had been identified using the citation ranking. Keyword co-occurrence analysis showed that the hotspots in ophthalmology research were epidemiological characteristics and treatment modalities of ocular diseases, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of ocular diseases. Keyword burst analysis revealed that “neural network,” “pharmacokinetics,” “geographic atrophy,” “implementation,” “variability,” “adverse events,” “automated detection,” and “retinal images” were the research trends of research in the field of ophthalmology through 2021. The analysis of the subject categories demonstrated the close cooperation relationships that existed between different subject categories, and collaborations with non-ophthalmology-related subject categories were increasing over time in the field of ophthalmology research.ConclusionsThe hotspots in ophthalmology research were epidemiology, prevention, screening, and treatment of ocular diseases, as well as artificial intelligence and fundus imaging technology and telemedicine. Research trends in ophthalmology research were artificial intelligence, drug development, and fundus diseases. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases

The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.

A Method for Diagnosing Diabetic Retinopathy Based on Ocular Fundus Imaging

Introduction. Diabetic retinopathy is a complication of diabetes mellitus caused by high blood sugar levels damaging the retina. Diabetic retinopathy leads to changes in ocular blood vessels and the appearance of solid exudates and microaneurysms. When diagnosed and treated in the late stages, this disease can cause blindness. The most common diagnostic method for diabetic retinopathy is based on ocular fundus imaging. However, the background interference and low contrast of such images significantly hinders the timely detection of vascular lesions. Therefore, the development of a method for detecting signs of diabetic retinopathy, particularly in its early stages, presents a relevant research task.Aim. Development of a method for diagnosing diabetic retinopathy based on an analysis of ocular fundus images using the decision-tree approach.Materials and methods. Methods based on image segmentation with identifying characteristic features and their binary classification were used. A verified database was used to access the accuracy of the proposed method for detecting diabetic retinopathy.Results. A method for detecting signs of diabetic retinopathy was developed, which includes the segmentation of vessels, exudates and microaneurysms based on digital processing of ocular vascular images using binary classification. The developed method showed a high level of diagnostic accuracy. Thus, the sensitivity, specificity and accuracy of diabetic retinopathy detection comprised 87.14, 88.50 and 87.81 %, respectively.Conclusion. The developed method allows diabetic retinopathy to be diagnosed with sufficiently high accuracy. The method can also be used for supporting decision making when managing patients with diabetic retinopathy.

Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poor-quality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning technique is used with various convolutional neural networks (CNN) models that automatically learn a thousand features in the digital retinal image, and are based on those features for diagnosing eye diseases. Comparison performance of many models is conducted to find the optimal model which fits with fundus classification. Among the different CNN models, DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99. Furthermore, the corresponding specificities for healthy, DR, GLC, and AMD patients are found to be 89.52%, 96.69%, 89.58%, and 100%, respectively. These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.

Cystatin C and cystatin SN as possible soluble tumor markers in malignant uveal melanoma.

BackgroundThe aim of the study was to determine the concentration of endogenous cystatin C and cystatin SN, as potential tumor biomarkers, in the serum and biological fluids of the eye in both healthy controls and patients with uveal melanoma.Patients and methodsThe concentration of both cystatins was determined in the intraocular fluid (IOF), tear fluid, and serum of patients with uveal melanoma and compared to baseline measurements in IOF, tears, serum, cerebral spinal fluid, saliva and urine of healthy controls.ResultsThe concentration of cystatin C in all the biological matrices obtained from healthy controls significantly exceeded the concentration of cystatin SN and was independent of gender. Cystatin C concentrations in the tear fluid of patients with uveal melanoma (both the eye with the malignancy, as well as the contralateral, non-affected eye), were significantly greater than cystatin C concentrations in the tear fluid of healthy controls and was independent of tumor size. The concentration of cystatin SN in IOF of patients with uveal melanoma was significantly less than the corresponding concentration of cystatin SN in healthy controls.ConclusionsThe ratio of cystatins (CysC:CysSN) in both the serum and tear fluid, as well as the concentration of cystatin SN in IOF, would appear to strongly suggest the presence of uveal melanoma. It is further suggested that multiple diagnostic criteria be utilized if a patient is suspected of having uveal melanoma, such as determination of the cystatin C and cystatin SN concentrations in serum, tears, and IOF, ocular fundus and ultrasound imaging, and biopsy with histopathological evaluation.

A Method of Blood Vessel Segmentation in Fundus Images Based on Image Enhancement

The changes of retinal vessel width are the key steps in the accurate diagnosis, early treatment and surgical planning of ocular diseases. The segmentation of retinal vessel is the basis of computer-aided diagnosis and large-scale disease screening. In this paper, we propose an algorithm for segmentation of fundus image based on image enhancement. At first, the ocular fundus image is enhanced by Laplacian pyramid, and the enhanced image vessel is more obvious than the background. Then, an improved matching filter algorithm is used to segment the ocular fundus image vessel. Experimental results show that the proposed method is more accurate than the traditional segmentation method. And it has strong robustness to the fundus images under different illumination conditions.

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model's solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

4-Aminopyridine, a Voltage-Gated K+ Channel Inhibitor, Attenuates Nitric Oxide-Mediated Vasodilation of Retinal Arterioles in Rats.

Nitric oxide (NO) is an important regulator of the retinal blood flow. The present study aimed to determine the role of voltage-gated K+ (KV) channels and ATP-sensitive K+ (KATP) channels in NO-mediated vasodilation of retinal arterioles in rats. In vivo, the retinal vasodilator responses were assessed by measuring changes in the diameter of retinal arterioles from ocular fundus images. Intravitreal injection of 4-aminopyridine (a KV channel inhibitor), but not glibenclamide (a KATP channel blocker), significantly attenuated the retinal vasodilator response to the NO donor (±)-(E)-4-ethyl-2-[(E)-hydroxyimino]-5-nitro-3-hexenamide (NOR3). Intravitreal injection of indomethacin (a non-selective cyclooxygenase inhibitor) also reduced the NOR3-induced retinal vasodilator response. The combination of 4-aminopyridine and indomethacin produced a greater reduction in the NOR3-induced response than either agent alone. 4-Aminopyridine had no significant effect on pinacidil (a KATP channel opener)-induced response. These results suggest that the vasodilatory effects of NO are mediated, at least in part, through the activation of 4-aminopyridine-sensitive KV channels in the retinal arterioles of rats. NO exerts its dilatory effect on the retinal vasculature of rats through at least two mechanisms, activation of the KV channels and enhancement of prostaglandin production.