— Clinically, using fundus pictures for predicting and detecting blind illnesses such as diabetic retinopathy (DR) is crucial. Deep learning (DL) is becoming a more common and promising technique in the different applications of DR, such as prediction, dete ction, classification, and disease diagnosis. Developing a review paper to analyze the DL techniques and their performance in the field is essential. We prepared a standard systematic review database including 341 publications. Accordingly, the main aim of the present review work is to present a systematic state -of-the-art by relying on PRISMA guidelines for the performance analysis of the DL in DR applications. The study has been shown in three main steps. The first step is to collect the database, the sec ond step is to analyze the databases, and the last step is to conclude the study's main findings. According to the results, most studies employed accuracy as the most reliable and general evaluation metric for analyzing the DL techniques in different DR ap plications. Also, CNN has the most share of applications compared to other DL techniques. On the other hand, the best performance is related to the ensemble and advanced DL techniques. We'll also publish and regularly update the most recent discoveries in future studies to stay up with the quick technological improvements. Keywords — Diabetic Retinopathy, Deep Learning, Systematic Review, PRISMA, Machine Learning I. INTRODUCTION A chronic disease called diabetic retinopathy (DR) can impair eyesight if it is not caught in time [1, 2] . The challenge of routinely diagnosing and identifying DRs is difficult and may need many exams. Most individuals worldwide are affected by diabetic retinopathy (DR), a concern for diabetes patients and one of the m ain causes of blindness [3]. The prevention or postponement of visual loss may be possible with early detection of DR [4]. These conditions can be efficiently detected with early examination of retinal fundus pictures, which includes optic disc and optic cup identification and segmentation of the retinal blood vessels [5]. The current techniques vary in performance and efficacy, cannot distinguish between distinct parts of the fundus picture, and are easily influenced by diseased areas [6, 7] . Expert manual visual inspection, which involved meticulously studying high -resolution color fundus photographs, was formerly the only method to diagnose these disorders. This method was expensive, time -consuming, and labor -intensive [8]. Recently, the use of Artificial intelligence (AI) based techniques has been significantly raised for different applications in different fields of the area [9-11]. Deep learning (DL), a subset of AI networks, has demonstrated excellent promise for illness prediction [12]. The literature review analysis reveals that DL methods can successfully assist in DR detection utilizing retinal pictures [13]. DL is a form of machine learning and AI that mimics how people study specific subjects. In data science, whi ch also encompasses statistics and predictive modeling, DL plays a significant role [14-16]. Review studies in a specialist field can assist in evaluating a particular policy's benefits and drawbacks and offer furthe r recommendations [17, 18] . Accordingl y, we developed a short review paper for comparing the performance of the DL techniques applied for diabetic retinopathy in fundus images. We also explored similar conducted review works for detecting five primary eye diseases —diabetic retinopathy, glaucom a, age -related macular degeneration, cataract, and retinopathy of prematurity. Goutam et al. (2022) gives a thorough analysis of the various deep learning techniques used recently. This article is structured along the lines of the deep learning implementati on process pipeline. Common datasets, evaluation metrics, image pre -processing methods, and deep learning backbone models are first illustrated, and then a thorough analysis of various approaches for each of the five retinal diseases mentioned is provided [19]. Jeong et al. (2022) presented a state -of-the-art piece using a color fundus image from one of the imaging modalities utilized in ophthalmology. Investigations focus on automated screening and diagnosis approaches using DL for glaucoma, age -related macular degeneration, and DR [20]. Shekar et al. (2021) presented a review paper intending to examine numerous pieces of existing literature that present various techniques for recognizing DR using DL and machine learning (ML) techniques, as well as to address the challenges associated with the various datasets that DR uses [21]. There are different review studies for considering DL in DR applications. But, a systematic review study for analyzing the performance of the DL techniques for DR is lost from the literature. Accordingly, the main aim of the present review work is to prese nt a systematic state -of-the-art by relying on PRISMA guidelines for the performance analysis of the DL in DR applications. The study has been shown in three main steps. The first step is to collect the database, the second step is to analyze the databases , and the last step is to conclude the study's main findings. II. METHODOLOGY Data collection has been conducted using standard PRISMA guidelines [22]. The first phase of the PRISMA guidelines, the identification phase, identifies the needed records and prepares the database ( Figure I , Phase I). In this phase, 489 records (about 95% of total records) were selected from the Thomson Reuters Web -of-Science (WoS), and Elsevier Scopus, whereas the rest which is 22 records (about 5% of total records) were selected from the other databases. The searching queries were {"diabetic retinopathy (DR)", "fundus image", "Gradient Descent (GD)", "Autoencoder", "Boltzmann Machines", "machine learning (ML)", "Self - Organizing Map (SOM)", "Generative Adversarial Network (GAN)", "deep lear ning (DL)", "Convolutional neural network (CNN)", "LSTM", "Recurrent neural network (RNN)" and "Deep reinforcement learning (DRL)". Records in the second phase, the screening phase ( Figure I , Phase II), were evaluated in terms of duplication and the chosen relevant cases. During duplication, around 10% of total records (53 records) were eliminated. Meanwhile, 48 records (about 9% of the total) were determined as redundant and deleted by analyzing the title and their abstracts. Hence 410 records (about 80% o f total records) were selected to be studied through Phase III, the eligibility phase (See Figure I , Phase III). In the third phase, the authors studied the full text of the selected records, and the most relevant cases were chosen during monitoring eligib ility. However, there were some limitations to have access to the full text of the records. Hence, almost 341 records (about 66% of total records) were chosen for the next evaluations. Despite what has been mentioned, there are still some conference articl es in which their full text was not available so it would be inevitable not being eliminated. It is the exact moment to generate the main database to do qualitative and numerical analysis. FIGURE I. PRISMA GUIDELINES By analyzing the prepared database from the output of the PRISMA guideline, the published case trend has been presented in Figure II . As is clear, the trend of the published articles on the subject is rising significantly. This demonstrates that we are facing an attractive and popular topic . Figure III presents the type of published cases. According to Figure III , conference papers followed by articles have the highest share of the published cases (about 90%). Figure IV presents the share of the frequently used keywords in the field, which has been extracted from the output of the systematic review section. According to Figure IV, computer science (about 62%) followed by Engineering and medicine are the three most frequently used keywords compared to others in the field. FIGURE II. THE TREND OF THE PUBLISHED CASES FIGURE III. THE SHARE OF THE DIFFERENT TYPES OF THE PUBLISHED CASES FIGURE IV. THE SHARE OF EACH FREQUENTLY USED KEYWORDS FIGURE V. THE SHARE OF EACH SOURCES Figure V presents the share of the sources published the most cases in the field of the study. Accordingly, Lecture notes in computer science followed by IEEE and Lecture notes in electrical engineering have owned the most share of the publications (about 30%). Table 1 presents the notable technical studies conducted for DL in DR applications in 2022. Table 1 has six columns for