Retinal Blood Vessels Research Articles

Retinal blood vessel diameter changes with 60-day head-down bedrest are unaffected by antioxidant nutritional cocktail.

Long-term human spaceflight can lead to cardiovascular deconditioning, but little is known about how weightlessness affects microcirculation. In this study, we examined how the retinal microvessels and cerebrovascular regulation of 19 healthy male participants responded to long-term head-down bedrest (HDBR), an earth-based analog for weightlessness. In addition, we examined whether an anti-inflammatory/antioxidant cocktail could prevent the vascular changes caused by HDBR. In all study participants, we found a decrease in retinal arteriolar diameter by HDBR day 8 and an increase in retinal venular diameter by HDBR day 16. Concurrently, blood pressure at the level of the middle cerebral artery and the cerebrovascular resistance index were higher during HDBR, while cerebral blood flow velocity was lower. None of these changes were reversed in participants receiving the anti-inflammatory/antioxidant cocktail, indicating that this cocktail was insufficient to restore the microvascular and cerebral blood flow changes induced by HDBR.

BreakNet: discontinuity-resilient multi-scale transformer segmentation of retinal layers

Visible light optical coherence tomography (vis-OCT) is gaining traction for retinal imaging due to its high resolution and functional capabilities. However, the significant absorption of hemoglobin in the visible light range leads to pronounced shadow artifacts from retinal blood vessels, posing challenges for accurate layer segmentation. In this study, we present BreakNet, a multi-scale Transformer-based segmentation model designed to address boundary discontinuities caused by these shadow artifacts. BreakNet utilizes hierarchical Transformer and convolutional blocks to extract multi-scale global and local feature maps, capturing essential contextual, textural, and edge characteristics. The model incorporates decoder blocks that expand pathways to enhance the extraction of fine details and semantic information, ensuring precise segmentation. Evaluated on rodent retinal images acquired with prototype vis-OCT, BreakNet demonstrated superior performance over state-of-the-art segmentation models, such as TCCT-BP and U-Net, even when faced with limited-quality ground truth data. Our findings indicate that BreakNet has the potential to significantly improve retinal quantification and analysis.

The effects of SOD Melon Gliadin as an Antioxidant on VEGF and TGF β blood serum and vitreoretina in hyperglycemia rat model a literature review

Diabetes Mellitus is a chronic metabolic disorder characterized by hyperglycemia resulting from impaired insulin secretion, action, or both. Persistent hyperglycemia can lead to organ damage, including ocular complications such as diabetic retinopathy. Diabetic retinopathy is a primary cause of preventable blindness and can become vision-threatening if complications are persistent. Over one-third of individuals with diabetes exhibit signs of diabetic retinopathy, and among them, approximately one-third experience vision-threatening diabetic retinopathy (VTDR), defined as severe non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or the presence of diabetic macular edema (DME). Diabetic retinopathy triggers the production of reactive oxygen species (ROS). Excessive accumulation of ROS can damage tissues surrounding retinal blood vessels, leading to oxidative stress that compromises the blood-retina barrier (BRB). Vascular endothelial growth factor (VEGF) and transforming growth factor-beta (TGF-β) are two markers that indicate retinal dysfunction. Cells possess an intrinsic defense system against ROS, comprising antioxidants such as superoxide dismutase (SOD), glutathione reductase, glutathione peroxidase, and catalase. SOD melon gliadin (Glisodin®) is a potent antioxidant that elevates endogenous SOD levels, reduces ROS, and protects retinal tissues from damage and cell death. Research on SOD melon gliadin (Glisodin®) shows its potential as an antioxidant for preventing diabetic complications in the retina, specifically diabetic retinopathy.

Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

TCDDU-Net: combining transformer and convolutional dual-path decoding U-Net for retinal vessel segmentation

Accurate segmentation of retinal blood vessels is crucial for enhancing diagnostic efficiency and preventing disease progression. However, the small size and complex structure of retinal blood vessels, coupled with low contrast in corresponding fundus images, pose significant challenges for this task. We propose a novel approach for retinal vessel segmentation, which combines the transformer and convolutional dual-path decoding U-Net (TCDDU-Net). We propose the selective dense connection swin transformer block, which converts the input feature map into patches, introduces MLPs to generate probabilities, and performs selective fusion at different stages. This structure forms a dense connection framework, enabling the capture of long-distance dependencies and effective fusion of features across different stages. The subsequent stage involves the design of the background decoder, which utilizes deformable convolution to learn the background information of retinal vessels by treating them as segmentation objects. This is then combined with the foreground decoder to form a dual-path decoding U-Net. Finally, the foreground segmentation results and the processed background segmentation results are fused to obtain the final retinal vessel segmentation map. To evaluate the effectiveness of our method, we performed experiments on the DRIVE, STARE, and CHASE datasets for retinal vessel segmentation. Experimental results show that the segmentation accuracies of our algorithms are 96.98, 97.40, and 97.23, and the AUC metrics are 98.68, 98.56, and 98.50, respectively.In addition, we evaluated our methods using F1 score, specificity, and sensitivity metrics. Through a comparative analysis, we found that our proposed TCDDU-Net method effectively improves retinal vessel segmentation performance and achieves impressive results on multiple datasets compared to existing methods.

AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography

Retinal vasculature is the only vascular system in the human body that can be observed in a non-invasive manner, with a phenotype associated with a wide range of ocular, cerebral, and cardiovascular diseases. OCT and OCT angiography (OCTA) provide powerful imaging methods to visualize the three-dimensional morphological and functional information of the retina. In this study, based on OCT and OCTA multimodal inputs, a multitask convolutional neural network model was built to realize 3D segmentation of retinal blood vessels and disease classification for different retinal diseases, overcoming the limitations of existing methods that can only perform 2D analysis of OCTA. Two hundred thirty sets of OCT and OCTA data from 109 patients, including 138,000 cross-sectional images in normal and diseased eyes (age-related macular degeneration, retinal vein occlusion, and central serous chorioretinopathy), were collected from four commercial OCT systems for model training, validation, and testing. Experimental results verified that the proposed method was able to achieve a DICE coefficient of 0.956 for 3D segmentation of blood vessels and an accuracy of 91.49% for disease classification, and further enabled us to evaluate the 3D reconstruction of retinal vessels, explore the interlayer connections of superficial and deep vasculatures, and reveal the 3D quantitative vessel characteristics in different retinal diseases.

Retraction Note: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

Retraction Note: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Repeated treatment with VEGF receptor inhibitors induces phenotypic changes in endothelial cells and pericytes in the rat retina

Abnormal ocular angiogenesis is a major cause of visual impairment and vision loss in neovascularization-related diseases. Currently, anti-vascular endothelial growth factor (VEGF) drugs are used to treat ocular neovascularization, but repeated injections are needed to maintain their therapeutic effects. However, repeated injection of anti-VEGF drugs may affect the retinal blood vessel phenotype and diminish therapeutic effects. In this study, we aimed to investigate the phenotypic changes in endothelial cells and pericytes caused by the repeated interruption of the VEGF receptor signaling pathway in neonatal rats. KRN633 (10 mg/kg), a VEGF receptor tyrosine kinase inhibitor, was subcutaneously administered on postnatal day (P)-7 and P8 (first round), P14 and P15 (second round), and P21 and P22 (third round). The rat eyes were collected on P7, P9, P14, P16, P21, P23, P28, and P35. Using retinal flat-mount specimens stained with specific markers for vascular endothelial cells, basement membranes, and pericytes, the arteriolar tortuosity, capillary area density, and distribution of pericytes were evaluated. Significant loss of capillaries was observed the day after the first round of KRN633 treatment, after which aggressive angiogenesis occurred, leading to the formation of tortuous arterioles. Rats that completed second and third rounds of KRN633 treatment showed more severe abnormalities in the retinal vasculature than those that only completed first round treatment. Repeated treatment with KRN633 decreased the anti-angiogenic effects but increased the immunoreactivity of α-smooth muscle actin in the pericytes on veins and capillaries. α-Smooth muscle actin expression was inversely correlated to anti-angiogenic effects. Overall, these results revealed that repeated interruption of VEGF receptor signaling pathway altered the phenotypes of endothelial cells and pericytes and induced anti-VEGF drug resistance. Therefore, careful follow-up is necessary when using anti-VEGF drugs to treat abnormal angiogenesis-associated ocular diseases.

Retraction Note: An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

Retraction Note: An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity.

Intracellular organelle networks (IONs) such as the endoplasmic reticulum (ER) network and the mitochondrial (MITO) network serve crucial physiological functions. The morphology of these networks plays a critical role in mediating their functions. Accurate image segmentation is required for analyzing the morphology and topology of these networks for applications such as molecular mechanism analysis and drug target screening. So far, however, progress has been hindered by their structural complexity and density. In this study, we first establish a rigorous performance baseline for accurate segmentation of these organelle networks from fluorescence microscopy images by optimizing a baseline U-Net model. We then develop the multi-resolution encoder (MRE) and the hierarchical fusion loss (Lhf) based on two inductive components, namely low-level features and topological self-similarity, to assist the model in better adapting to the task of segmenting IONs. Empowered by MRE and Lhf, both U-Net and Pyramid Vision Transformer (PVT) outperform competing state-of-the-art models such as U-Net++, HR-Net, nnU-Net, and TransUNet on custom datasets of the ER network and the MITO network, as well as on public datasets of another biological network, the retinal blood vessel network. In addition, integrating MRE and Lhf with models such as HR-Net and TransUNet also enhances their segmentation performance. These experimental results confirm the generalization capability and potential of our approach. Furthermore, accurate segmentation of the ER network enables analysis that provides novel insights into its dynamic morphological and topological properties. Code and data are openly accessible at https://github.com/cbmi-group/MRE.

Diabetic retinopathy detection and categorizing using a lightweight deep learning approach

Diabetic retinopathy is an ocular disorder that has the potential to result in visual impairment and complete loss of vision in those diagnosed with diabetes. This illness affects the retinal blood vessels inside the light-sensitive tissue layer at the posterior of the eye, known as the retina. This paper presents a complete approach to diagnosing and categorizing diabetic retinopathy using deep learning models. A lightweight Convolutional Neural Network (CNN) is used to detect diabetic retinopathy in fundus images. This CNN has been developed to have fewer parameters and calculations, making it suited for resource-constrained environments while retaining decent performance. The categorization of diabetic retinopathy is carried out with the help of EfficientNet. This model uses an innovative compound scaling approach to strike a balance between the model's depth, width, and resolution. As a result, it maximizes computing efficiency while preserving high accuracy. The proposed detection model obtained an accuracy of 95%, and the classification model produced an accuracy of 84%.

An Investigation of the Correlation Between Retinal Nerve Fiber Layer Thickness with Blood Biochemical Indices and Cognitive Dysfunction in Patients with Type 2 Diabetes Mellitus.

The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications. Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed. Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses (P<0.01). There existed no significant difference in blood pressure, body mass index (BMI), blood lipids (triglycerides, cholesterol, low-density lipoprotein, high-density lipoprotein) between the two groups (P>0.05). Compared with the simple diabetes group, patients in the DR group had much higher fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting insulin (FINS), insulin resistance (HOMA-IR) index, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1) (P<0.001). Besides, the DR group had sharply lower scores on the Mini-Mental State Examination (MMSE) scale and higher levels of the Trail Making Test-A (TMT-A) and TMT-B (P<0.001). Spearman correlation analysis confirmed that the mean RNFLT was negatively correlated with the levels of FBG, HbA1c, HOMA-IR index, TMT-A and TMT-B (P<0.05), positively correlated with the score of mini-mental state examination (MMSE) (P<0.05), and was no significant correlation with FINS and ApoB/ApoA1 (P>0.05). DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

Isolation of Primary Mouse Retinal Glial Müller Cells.

The primary supporting cell of the retina is the retinal glial Müller cell. They cover the entire retinal surface and are in close proximity to both the retinal blood vessels and the retinal neurons. Because of their growth, Müller cells perform several crucial tasks in a healthy retina, including the uptake and recycling of neurotransmitters, retinoic acid compounds, and ions (like potassium K+). In addition to regulating blood flow and maintaining the blood-retinal barrier, they also regulate the metabolism and the supply of nutrients to the retina. An established procedure for isolating primary mouse Müller cells is presented in this manuscript. To better understand the underlying molecular processes involved in the various mouse models of ocular disorders, Müller cell isolation is an excellent approach. This manuscript outlines a detailed procedure for Müller cell isolation from mice. From enucleation to seeding, the entire process lasts about a few hours. For 5-7 days after seeding, the media shouldn't be changed in order to allow the isolated cells to grow unhindered. Cell characterization using morphology and distinct immunofluorescent markers comes next in the process. Maximum passages for cells are 3-4 times.

The Impact of High-Dose Fish Oil Supplementation on Mfsd2a, Aqp4, and Amyloid-β Expression in Retinal Blood Vessels of 5xFAD Alzheimer's Mouse Model.

In patients with Alzheimer's disease (AD) and in animal models, the increased accumulation of amyloid β (Aβ) in retinal blood vessels strongly correlates with brain amyloid deposits and cognitive decline. The accumulation of Aβ in blood vessels may result from impaired transcytosis and a dysfunctional ocular glymphatic system in AD. High-dose fish oil (FO) supplementation has been shown to significantly change the expression of major facilitator superfamily domain-containing protein 2a (Mfsd2a), a key regulator of transcytosis, and Aquaporin 4 (Aqp4), an essential component of the glymphatic system in the retinas of WT mice. We examined the expression of Mfsd2a and Aqp4 in the retinas of 4-month-old 5xFAD female mice supplemented with high-dose FO for three weeks. There was a significant increase in Mfsd2a expression in 5xFAD retinas supplemented with FO compared to control 5xFAD mice. Additionally, the increase in Aqp4 expression observed in 4-month-old 5xFAD retinas, indicative of an impaired glymphatic system, was significantly decreased. Simultaneously, Aβ accumulation in 5xFAD retinal blood vessels was reduced following FO supplementation. These findings suggest that high-dose FO supplementation could serve as an adjunct in developing new treatments aimed at improving the regulation of transcytosis or the function of the glymphatic system in the AD retina.

Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images.

The state of the human eye's blood vessels is a crucial aspect in the diagnosis of ophthalmological illnesses. For many computer-aided diagnostic systems, precise retinal vessel segmentation is an essential job. However, it remains a difficult task due to the intricate vascular system of the eye. Although many different vascular segmentation techniques have already been presented, additional study is still required to address the problem of inadequate segmentation of thin and tiny vessels. In this work, we introduce the Spatial Attention U-Net (SAU-Net) modelwith harris hawks' optimization (HHO), a lightweight network that can be applied as a data augmentation technique to improve the efficiency of the existingannotated samples without the need of thousands of training instances for Retinal Blood Vessel and Optic Disc Segmentation. The SAU-Net-HHO implementation uses a spatially inferred attention map multiplied by the input feature map for adaptive feature enhancement. U-Net convolutional blocks have been replaced with structured dropout blocks in the proposed network to prevent overfitting. Data from both vascular extraction (DRIVE) and structured analysis of the retina (STARE) are used to evaluate SAU-Net-HHO performance. The results show that the proposed SAU-Net-HHO performs well on both datasets. Analysing the obtained results, an average of 98.5% accuracy and Specificity 96.7% was achieved for DRIVE dataset and 97.8% accuracy and specificity 94.5% for STARE dataset. The proposed method yields numerical results with average values that are on par with those of state-of-the-art methods. Visual inspection has revealed that the suggested method can segment thin and tiny vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

Assessment of retinal blood vessel segmentation using U-Net model: A deep learning approach

Segmentation of retinal blood vessels from fundus images is vital to assist ophthalmologists in diagnosing different eye diseases like Arteriosclerosis, Glaucoma, Diabetic Retinopathy, Hypertension, and Choroidal Neovascularization. Accurate detection and timely treatment of these eye diseases can prevent irreversible impairment of vision. In computer-aided automatic diagnosis, Preprocessing of fundus images is necessary to reduce the uneven retinal image illumination, as well as the undesirable effect arising from the contrast differences between the retinal blood vessels and the background. In this study, the Hessian-based Frangi vesselness filter is proposed to enhance vasculature in fundus images. This approach seeks to extract thin vessels to diminish the intensity disparity between thick and thin vessels. Its accuracy has never, however, been thoroughly evaluated. To verify the effectiveness of the suggested filter for boosting vessel-like structures in the fundus images, an experimental approach is presented in this paper. The ability of the filter to improve vessel-like structures in fundus images is validated, and an experimental technique for evaluating filter performance using the U-Net model is described. Five quantitative performances, namely Accuracy, Recall, Specificity, Precision, and F1 Score measures on the DRIVE, HRF, DIARETDB1, DIARETDB0, CHASEDB1, ORIGA, DRISHTI GS, DRIONS DB, FIRE, and FIOT datasets, were evaluated and quantified in these experiments to validate the efficacy of the proposed filter. The results demonstrated a noteworthy improvement by achieving an Accuracy of 99.79 %, 99.84 %, 99.64 %, 99.72 %, 99.57 %, 99.48 %, 99.77 %, 99.05 %, 99.93 %, and 99.40 % respectively. Empirical evidence supports the advantages of this approach.

Retinal microvascular changes in patients with pancreatitis and their clinical significance

Acute pancreatitis, a common exocrine inflammatory disease affecting the pancreas, is characterized by intense abdominal pain and multiple organ dysfunction. However, the alterations in retinal blood vessels among individuals with acute pancreatitis remain poorly understood. This study employed optical coherence tomography angiography (OCTA) to examine the superficial and deep retinal blood vessels in patients with pancreatitis. Sixteen patients diagnosed with pancreatitis (32 eyes) and 16 healthy controls (32 eyes) were recruited from the First Affiliated Hospital of Nanchang University for participation in the study. Various ophthalmic parameters, such as visual acuity, intraocular pressure, and OCTA image for retina consisting of the superficial retinal layer (SRL) and the deep retinal layer (DRL), were recorded for each eye. The study observed the superficial and deep retinal microvascular ring (MIR), macrovascular ring (MAR), and total microvessels (TMI) were observed. Changes in retinal vascular density in the macula through annular partitioning (C1–C6), hemispheric quadrant partitioning (SR, SL, IL, and IR), and early diabetic retinopathy treatment studies (ETDRS) partitioning methods (R, S, L, and I). Correlation analysis was employed to investigate the relationship between retinal capillary density and clinical indicators. Our study revealed that in the superficial retinal layer, the vascular density of TMI, MIR, MAR, SR, IR, S, C2, C3 regions were significantly decreased in patients group compared with the normal group. For the deep retinal layer, the vascular density of MIR, SR, S, C1, C2 regions also reduced in patient group. The ROC analysis demonstrated that OCTA possesses significant diagnostic performance for pancreatitis. In conclusion, patients with pancreatitis may have retinal microvascular dysfunction, and OCTA can be a valuable tool for detecting alterations in ocular microcirculation in pancreatitis patients in clinical practice.