Fundus Diseases Research Articles

Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network.

Hyperreflective foci (HF) reflects inflammatory responses for fundus diseases such as diabetic macular edema (DME), retina vein occlusion (RVO), and central serous chorioretinopathy (CSC). Shown as high contrast and reflectivity in optical coherence tomography (OCT) images, automatic segmentation of HF in OCT images is helpful for the prognosis of fundus diseases. Previous traditional methods were time-consuming and required high computing power. Hence, we proposed a lightweight network to segment HF (with a speed of 57ms per OCT image, at least 150ms faster than other methods). Our framework consists of two stages: an NLM filter and patch-based split to preprocess images and a lightweight DBR neural network to segment HF automatically. Experimental results from 3000 OCT images of 300 patients (100 DME,100 RVO, and 100 CSC) revealed that our method achieved HF segmentation successfully. The DBR network had the area under curves dice similarity coefficient (DSC) of 83.65%, 76.43%, and 82.20% in segmenting HF in DME, RVO, and CSC on the test cohort respectively. Our DBR network achieves at least 5% higher DSC than previous methods. HF in DME was more easily segmented compared with the other two types. In addition, our DBR network is universally applicable to clinical practice with the ability to segment HF in a wide range of fundus diseases.

Adversarial learning-based multi-level dense-transmission knowledge distillation for AP-ROP detection.

The Aggressive Posterior Retinopathy of Prematurity (AP-ROP) is the major cause of blindness for premature infants. The automatic diagnosis method has become an important tool for detecting AP-ROP. However, most existing automatic diagnosis methods were with heavy complexity, which hinders the development of the detecting devices. Hence, a small network (student network) with a high imitation ability is exactly needed, which can mimic a large network (teacher network) with promising diagnostic performance. Also, if the student network is too small due to the increasing gap between teacher and student networks, the diagnostic performance will drop. To tackle the above issues, we propose a novel adversarial learning-based multi-level dense knowledge distillation method for detecting AP-ROP. Specifically, the pre-trained teacher network is utilized to train multiple intermediate-size networks (i.e., teacher-assistant networks) and one student network by dense transmission mode, where the knowledge from all upper-level networks is transmitted to the current lower-level network. To ensure that two adjacent networks can distill the abundant knowledge, the adversarial learning module is leveraged to enforce the lower-level network to generate the features that are similar to those of the upper-level network. Extensive experiments demonstrate that our proposed method can realize the effective knowledge distillation from the teacher to student networks. We achieve a promising knowledge distillation performance for our private dataset and a public dataset, which can provide a new insight for devising lightweight detecting systems of fundus diseases for practical use.

Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases

Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the eye’s vision. Nevertheless, an eye may be afflicted with several diseases if proper care is not taken. A single retinal fundus image might be linked to one or more diseases. Age-related macular degeneration, cataracts, diabetic retinopathy, Glaucoma, and uncorrected refractive errors are the leading causes of visual impairment. Our research team at the center of excellence lab has generated a new dataset called the Retinal Fundus Multi-Disease Image Dataset 2.0 (RFMiD2.0). This dataset includes around 860 retinal fundus images, annotated by three eye specialists, and is a multiclass, multilabel dataset. We gathered images from a research facility in Jalna and Nanded, where patients across Maharashtra come for preventative and therapeutic eye care. Our dataset would be the second publicly available dataset consisting of the most frequent diseases, along with some rarely identified diseases. This dataset is auxiliary to the previously published RFMiD dataset. This dataset would be significant for the research and development of artificial intelligence in ophthalmology.

Eyedrop-based macromolecular ophthalmic drug delivery for ocular fundus disease treatment.

Therapeutic antibodies are extensively used to treat fundus diseases by intravitreal injection, as eyedrop formulation has been rather challenging due to the presence of ocular barriers. Here, an innovative penetrating carrier was developed for antibody delivery in eyedrop formulations. We found that fluorocarbon-modified chitosan (FCS) would self-assemble with proteins to form nanocomplexes, which could effectively pass across the complicated ocular structure to reach the posterior eye segments in both mice and rabbits. In a choroidal melanoma-bearing mouse model, eyedrops containing FCS/anti-PDL1 could induce stronger antitumor immune responses than those triggered by intravenous injection of anti-PDL1. Moreover, in choroidal neovascularization-bearing mouse and rabbit models, FCS/anti-VEGFA eyedrops effectively inhibited vascular proliferation, achieving comparable therapeutic responses to those observed with intravitreal injection of anti-VEGFA. Our work presents an effective delivery carrier to treat fundus diseases using eyedrop of therapeutic proteins, which may enable at-home treatment of many eye diseases with great patient compliance.

Automatic Multilabel Classification of Multiple Fundus Diseases Based on Convolutional Neural Network With Squeeze-and-Excitation Attention.

Automatic multilabel classification of multiple fundus diseases is of importance for ophthalmologists. This study aims to design an effective multilabel classification model that can automatically classify multiple fundus diseases based on color fundus images. We proposed a multilabel fundus disease classification model based on a convolutional neural network to classify normal and seven categories of common fundus diseases. Specifically, an attention mechanism was introduced into the network to further extract information features from color fundus images. The fundus images with eight categories of labels were applied to train, validate, and test our model. We employed the validation accuracy, area under the receiver operating characteristic curve (AUC), and F1-score as performance metrics to evaluate our model. Our proposed model achieved better performance with a validation accuracy of 94.27%, an AUC of 85.80%, and an F1-score of 86.08%, compared to two state-of-the-art models. Most important, the number of training parameters has dramatically dropped by three and eight times compared to the two state-of-the-art models. This model can automatically classify multiple fundus diseases with not only excellent accuracy, AUC, and F1-score but also significantly fewer training parameters and lower computational cost, providing a reliable assistant in clinical screening. The proposed model can be widely applied in large-scale multiple fundus disease screening, helping to create more efficient diagnostics in primary care settings.

Efficacy of regional anesthesia in episcleral surgery: a randomized controlled double-blind clinical study

BACKGROUND: Surgical interventions for severe fundus diseases associated with diabetes mellitus, proliferative processes against the background of retinal detachment, and acute inflammatory diseases require the development of multimodal algorithms to provide sufficient analgesic and anti-inflammatory effects.
 AIM: To evaluate the efficacy of combined pterygopalatine blockade (PPB) and retrobulbar blockade (RBB) in patients with episcleral retinal detachment surgery.
 MATERIALS AND METHODS: The study included 100 patients who underwent episcleral surgery for retinal detachment. All patients were randomized into two groups: group 1 (retrobulbar blockade, n=50) and group 2 (pterygopalatine blockade in combination with retrobulbar blockade, n=50). The level of akinesia, volume of local anesthetic, consumption of analgesics, pain, and postoperative nausea and vomiting (PONV) were evaluated.
 RESULTS: In group 2, only 12% of the patients were prescribed promedol 2% intraoperatively, which is significantly lower than that in group 1 (p=0.463). In group 2, the pain level 8 h after surgery according to the verbal sensation scale was 0.80.63. In group 1, the average pain score was 1.691.8 (p=0.558). PONV was observed in 4% of the patients in group 1. In group 2, no PONV occurred (p=0.563).
 CONCLUSION: The combination of PPB and RBB allows for controlling not only pain sensitivity but also vegetative, motor, and neuroendocrine manifestations associated with pain both in the intraoperative and early postoperative periods.

Combining convolutional neural networks and self-attention for fundus diseases identification

Early detection of lesions is of great significance for treating fundus diseases. Fundus photography is an effective and convenient screening technique by which common fundus diseases can be detected. In this study, we use color fundus images to distinguish among multiple fundus diseases. Existing research on fundus disease classification has achieved some success through deep learning techniques, but there is still much room for improvement in model evaluation metrics using only deep convolutional neural network (CNN) architectures with limited global modeling ability; the simultaneous diagnosis of multiple fundus diseases still faces great challenges. Therefore, given that the self-attention (SA) model with a global receptive field may have robust global-level feature modeling ability, we propose a multistage fundus image classification model MBSaNet which combines CNN and SA mechanism. The convolution block extracts the local information of the fundus image, and the SA module further captures the complex relationships between different spatial positions, thereby directly detecting one or more fundus diseases in retinal fundus image. In the initial stage of feature extraction, we propose a multiscale feature fusion stem, which uses convolutional kernels of different scales to extract low-level features of the input image and fuse them to improve recognition accuracy. The training and testing were performed based on the ODIR-5k dataset. The experimental results show that MBSaNet achieves state-of-the-art performance with fewer parameters. The wide range of diseases and different fundus image collection conditions confirmed the applicability of MBSaNet.

Evaluation of Fundus Function in Mature Cataract Patients by Visual Electrophysiology

Purpose. To explore the value of visual electrophysiology in evaluating the fundus function of mature cataract patients. Methods. 124 mature cataract patients (153 eyes) were examined before cataract surgery; the examinations included best corrected visual acuity (BCVA), pattern visual evoked potential (PVEP), full‐field electroretinogram (ffERG), and multifocal electroretinogram (mfERG). According to the postoperative fundus conditions, the subjects were divided into two groups: the no fundus disease group and the fundus disease group. Approximately one month after the operation, BCVA was measured, and visual electrophysiology was performed on subjects who had a stable fundus condition and had not received treatment for fundus disease. Results. One month after cataract surgery, BCVA ≤ 0.3 logMAR was found in 60 eyes (96.8%) without fundus disease and 59 eyes (64.8%) with fundus disease. Compared with the group without fundus disease, the preoperative electrophysiological examination of the group with fundus disease showed that the amplitude of ffERG waves and the amplitude density of the P1 wave in the 2nd to 5th rings of mfERG were decreased (all P < 0.05). ffERG and mfERG can be used for differential diagnosis of fundus disease (all P < 0.05), while PVEP has no significant diagnostic value for fundus disease (all P > 0.05). In the group without fundus disease, the amplitude of the PVEP 15′ P100 wave and the amplitude of dark‐adapted (DA) 0.01 b‐wave, DA 3.0 a‐wave, and DA 10.0 a‐wave were negatively correlated with postoperative logMAR BCVA (all P < 0.05). In the group with fundus disease, the amplitude of PVEP and ffERG and the amplitude density of mfERG were negatively correlated with postoperative logMAR BCVA (all P < 0.05). In the eyes of cortical cataracts, some parameters of PVEP, ffERG, and mfERG were significantly different before and after surgery. In the eyes of nuclear cataracts, some parameters of ffERG and mfERG were significantly different before and after surgery. In the eyes of posterior subcapsular cataracts, some parameters of PVEP and ffERG were significantly different before and after surgery. Conclusions. ffERG and mfERG can be used to detect fundus disease in mature cataract patients. The preoperative visual electrophysiological examination has high clinical value in predicting postoperative vision of mature cataract patients with fundus disease. Different types of cataracts have different effects on electrophysiological examination results. When interpreting the electrophysiological report, it is necessary to consider the existence of cataracts. This trial is registered with 2019-K068.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives.

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

Detection of Diabetic Fundus Disease Based on Deep Learning

Detection of Diabetic Fundus Disease Based on Deep Learning

Retinal blood vessels segmentation using CNN algorithm

The precise identification of blood vessels in fundus is crucial for diagnosing fundus diseases. In order to address the issues of inaccurate segmentation and low precision in conventional retinal image analysis for segmentation methods, a new approach was developed.The suggested method merges the U-Net and Dense-Net approaches and aims to enhance vascular feature information. To achieve this, the method employs several techniques such asHistogram equalization with limited contrast enhancement, median filtering, normalization of data, and morphological transformation. Furthermore, to correct artifacts, the method utilizes adaptive gamma correction. Next, randomly selected image blocks are utilized as training data to expand the data and enhance the generalization capability. The Dice loss function was optimized using stochastic gradient descent to improve the accuracy of segmentation, and ultimately, the Dense-U-net model was used for performing the segmentation. The algorithm achieved specificity, accuracy, sensitivity, and AUC of 0.9896, 0.9698, 0.7931, and 0.8946 respectively, indicating significant improvement in vessel segmentation accuracy, particularly in identifying small vessels.

Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images.

To design and evaluate a deep learning model based on ultra-widefield images (UWFIs) that can detect several common fundus diseases. Based on 4574 UWFIs, a deep learning model was trained and validated that can identify normal fundus and eight common fundus diseases, namely referable diabetic retinopathy, retinal vein occlusion, pathologic myopia, retinal detachment, retinitis pigmentosa, age-related macular degeneration, vitreous opacity, and optic neuropathy. The model was tested on three test sets with data volumes of 465, 979, and 525. The performance of the three deep learning networks, EfficientNet-B7, DenseNet, and ResNet-101, was evaluated on the internal test set. Additionally, we compared the performance of the deep learning model with that of doctors in a tertiary referral hospital. Compared to the other two deep learning models, EfficientNet-B7 achieved the best performance. The area under the receiver operating characteristic curves of the EfficientNet-B7 model on the internal test set, external test setA and external test setB were 0.9708 (0.8772, 0.9849) to 1.0000 (1.0000, 1.0000), 0.9683 (0.8829, 0.9770) to 1.0000 (0.9975, 1.0000), and 0.8919 (0.7150, 0.9055) to 0.9977 (0.9165, 1.0000), respectively. On a dataset of 100 images, the total accuracy of the deep learning model was 93.00%, the average accuracy of three ophthalmologists who had been working for 2years and three ophthalmologists who had been working in fundus imaging for more than 5years was 88.00% and 94.00%, respectively. High performance was achieved on all three test sets using our UWFI multidisease classification model with a small sample size and fast model inference. The performance of the artificial intelligence model was comparable to that of a physician with 2-5years of experience in fundus diseases at a tertiary referral hospital. The model is expected to be used as an effective aid for fundus disease screening.

Framework Nucleic Acids as Blood-Retinal-Barrier-Penetrable Nanocarrier for Periocular Administration.

Designing an ocular drugs delivery system that can permeate the outer blood-retinal barrier (oBRB) is crucial for the microinvasive or noninvasive treatment of ocular fundus diseases. However, due to the lack of a nanocarrier that can maintain structure and composition at the oBRB, only intravitreal injection at the eyeball can deliver therapeutics directly to the ocular fundus via paracellular and intercellular routes, despite the intraocular operations risks. Here, we demonstrated tetrahedral framework nucleic acids (tFNAs) can penetrate the oBRB and deliver therapeutic nucleic acids to the retina of the rat eye in vivo following subconjunctival injection. We also discovered that tFNAs were transported via a paracellular route across the intercellular tight junctions at the oBRB. The histology analysis for ocular layers indicated that individual and aptamer/doxorubicin-loaded tFNAs penetrated all layers of the posterior segment of the eyeball to reach the innermost retina and persisted for over 3 days with minimal systemic biodistribution. We expect that the programmability and penetrability of tFNAs will provide a promising method for drug delivery across oBRB and long-term sustenance at the target site via periocular administration to various tissues.

Chinese expert consensus on the ethical review of clinical research of ophthalmic gene therapy (2022)

The eye is considered as the best targeted organ for gene therapy, which has become a hot field of ophthalmic clinical trials in recent years. Since the risk of gene therapy is usually high, it is important to standardize the ethical review of related clinical research, so as to better protect the rights of patients and promote the innovative research. Thus, after several rounds of discussion, the experts from Chinese Vitreo-Retina Society of Chinese Medical Association and Fundus Disease Committee of Chinese Ophthalmologist Association have proposed the formal ethical review guidelines (International Practice Guideline Registration Number: IPGRP-2022CN347), which would play a role in standardizing clinical practice with gene therapy.

A few-shot learning-based eye diseases screening method.

This study aims to construct a brand-new ophthalmic disease screening task and establish a practically valuable ophthalmic disease screening model in the case of insufficient data. The main methods are as follows: firstly, we mixed data from different sources (these data may come from different cameras, including different fundus diseases) to get a new dataset. Based on this dataset, we conducted subsequent experiments on fundus multi-disease screening. However, in the past public datasets, each dataset often only corresponded to the screening diagnosis of one disease. Secondly, we proposed a method to simulate the characteristics of different fundus cameras by using a method based on style transfer, and to augment the training data, so that the model could learn the features of ophthalmic diseases in a more comprehensive way. Finally, a robust disease screening model based on few-shot learning was constructed on the combined dataset, and compared with benchmark algorithms. We focused on the study of eye disease screening methods based on the metric-based few-shot learning model, data augmentation methods, and focus on key technologies such as data augmentation based on style transfer. Experiments have shown that our method can significantly improve the generalization ability of the disease screening model. By introducing few-shot learning theory and data augmentation based on style transfer into ophthalmic disease screening, the generalization ability of the model is greatly improved, and it has certain practical value.

Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program.

Background: Artificial intelligence (AI) has been successfully applied to the screening tasks of fundus diseases. However, few studies focused on the potential of AI to aid medical teaching in the residency training program. This study aimed to evaluate the effectiveness of the AI-based pathologic myopia (PM) identification system in the ophthalmology residency training program and assess the residents' feedback on this system. Materials and Methods: Ninety residents in the ophthalmology department at the Second Affiliated Hospital of Zhejiang University were randomly assigned to three groups. In group A, residents learned PM through an AI-based PM identification system. In group B and group C, residents learned PM through a traditional lecture given by two senior specialists independently. The improvement in resident performance was evaluated by comparing the pre-and post-lecture scores of a specifically designed test using a paired t-test. The difference among the three groups was evaluated by one-way ANOVA. Residents' evaluations of the AI-based PM identification system were measured by a 17-item questionnaire. Results: The post-lecture scores were significantly higher than the pre-lecture scores in group A (p < 0.0001). However, there was no difference between pre-and post-lecture scores in group B (p = 0.628) and group C (p = 0.158). Overall, all participants were satisfied and agreed that the AI-based PM identification system was effective and helpful to acquire PM identification, myopic maculopathy (MM) classification, and "Plus" lesion localization. Conclusion: It is still difficult for ophthalmic residents to promptly grasp the knowledge of identification of PM through a single traditional lecture, while the AI-based PM identification system effectively improved residents' performance in PM identification and received satisfactory feedback from residents. The application of the AI-based PM identification system showed advantages in promoting the efficiency of the ophthalmology residency training program.

Prevalence, Characteristics, and Risk Factors of Retinal Hemorrhage among Full-Term Neonates in Southern China.

Neonatal retinal hemorrhage (RH) is the most common ocular fundus disease among newborns. Early detection and timely intervention are vital for reducing the risk of visual impairment caused by RH. However, little is known about the prevalence, characteristics, and risk factors of RH in southern China. Full-term infants born in Qingyuan City during the first 10 days of each month in 2021 were included in this study. All infants underwent RetCam III retinal examinations. Detailed information on retinal hemorrhage, including involved eyes, bleeding severity, and affected area (extrafoveal macula, fovea, or optic disc), and clinical information on the neonates and their mothers was collected. The results showed that among the 1072 eligible neonates, 266 (24.8%) had neonatal retinal hemorrhage. Consistent bilateral retinal hemorrhage severity was observed in 83.2% of the cases. The prevalence of optic disc involved RH, extrafoveal macular involved RH and foveal involved RH were 23.7%, 81.2% and 2.63%, respectively. Multivariate logistic regression analysis showed that lower birth weight (OR, 0.63; 95% CI, 0.40-0.99; p < 0.05) and vaginal delivery (OR, 20.6; 95% CI, 9.10-46.5; p < 0.001) were risk factors of neonatal RH. The area under the ROC curve of vaginal delivery, combined with birth weight, as predictors of neonatal RH was 0.73, with 85.3% sensitivity and 23.9% specificity. The birth weight cutoff was 3460 g. Our results suggested that neonatal RH is common in full-term neonates in southern China. It usually has the same severity in both eyes and mostly involves the extrafoveal macular region. Vaginal delivery and low birth weight are risk factors for neonatal RH.

Application Effect of Nursing Procedure in the Overall Nursing of Ocular Fundus Disease and Effect Analysis of Improving Cognition

Application Effect of Nursing Procedure in the Overall Nursing of Ocular Fundus Disease and Effect Analysis of Improving Cognition

Prognosis value of Chinese Ocular Fundus Diseases Society classification for proliferative diabetic retinopathy on postoperative visual acuity after pars plana vitrectomy in type 2 diabetes.

To compare the postoperative visual acuity among eyes with proliferative diabetic retinopathy (PDR) of different stages after pars plana vitrectomy (PPV) in type 2 diabetic patients. A retrospective study was conducted for PDR eyes undergoing PPV in type 2 diabetic patients. All patients were divided into three groups based on Chinese Ocular Fundus Diseases Society (COFDS) classification for PDR: Group A (primary vitreous hemorrhage), Group B (primary fibrovascular proliferation) and Group C (primary vitreous hemorrhage and/or fibrovascular proliferative combined with retinal detachment). The postoperative visual acuity and the change between postoperative and preoperative visual acuity were compared among three groups. The associated risk factors for postoperative visual acuity were analyzed in the univariate and multiple linear aggression. In total, 195 eyes of 195 patients were collected in this study, including 71 eyes of 71 patients in Group A, 75 eyes of 75 patients in Group B and 49 eyes of 49 patients in Group C. The eyes in Group A got better postoperative best-corrected visual acuity (BCVA) compared to the eyes in Group B and C (0.48±0.48 vs 0.89±0.63, P<0.001; 0.48±0.48 vs 1.04±0.67, P<0.001; respectively). The eyes in Group A got more improvement of BCVA compared to the eyes in Group B and C (1.07±0.70 vs 0.73±0.68, P=0.004; 1.07±0.70 vs 0.77±0.78, P=0.024; respectively). In the multiple linear regression analysis, primary fibro-proliferative type (β=0.194, 95%CI=0.060-0.447, P=0.01), retinal detachment type (β=0.244, 95%CI=0.132-0.579, P=0.02), baseline logMAR BCVA (β=0.192, 95%CI=0.068-0.345, P=0.004), silicone oil tamponade (β=0.272, 95%CI=0.173-0.528, P<0.001) was positively correlated with postoperative logMAR BCVA. Eyes undergoing phacovitrectomy had better postoperative BCVA (β=-0.144, 95%CI=-0.389 to -0.027, P=0.025). PDR eyes of primary vitreous hemorrhage type usually have better visual acuity prognosis compared to primary fibrovascular proliferation type and retinal detachment type. COFDS classification for PDR may have a high prognostic value for postoperative visual outcome and surgical management indications.

Effects of the Implementation of an Emergency Surgical Pattern in Patients with Rhegmatogenous Retinal Detachment: A Retrospective Observational Study.

Background To analyze the effects of the implementation of emergency surgical patterns in patients with rhegmatogenous retinal detachment (RRD) and provide evidence for promoting emergency surgical patterns for RRD. Methods We reviewed the electronic medical records of 346 patients (348 eyes) who underwent surgical repair of RRD at the Zhongshan Ophthalmic Center in Southern China. A total of 140 patients (140 eyes) in the routine inpatient surgery group were collected at the fundus disease department between January 2019 and December 2019, and 206 patients (208 eyes) in the emergency surgery group were collected at the ophthalmic emergency department between January 2021 and December 2021. Demographics, best-corrected visual acuity (BCVA) expressed as the logarithm of the minimum angle of resolution (logMAR), the status of the macula before surgery, time to presentation, treatment interval, and postoperative BCVA measured at least three months follow-up were compared. Results The preoperative BCVA (logMAR) of the emergency surgery group and the inpatient surgery group were 1.0 (0.4–1.7) and 1.4 (0.7–1.7), respectively, with significant differences between groups (P < 0.001). However, patients had a shorter time to presentation (7 days vs. 21 days, P < 0.001), shorter treatment interval (2 days vs. 12 days, P < 0.01), and significantly better postoperative BCVA (logMAR 0.5 vs. logMAR 1.0, P < 0.001) in the emergency surgery group than in the inpatient surgery group. There was no significant difference in primary anatomical success between the two groups (P=0.802). The median follow-up for the emergency surgery group and the inpatient surgery group were 6.08 months and 6.2 months, respectively, with no significant differences (P > 0.05). Conclusions Patients who underwent emergency surgical patterns of RRD had better visual outcomes after surgery than patients with routine inpatient surgery, which might be attributed to a shorter duration, shorter treatment interval, and the preoperative status of the macula in the emergency surgery pattern. Emergency surgical patterns for RRD should be considered to achieve better surgical outcomes in suitable patients.