Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Abstract

Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.

Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

“…The main objective of applying AI to healthcare is to unfold hidden information from big data and assist healthcare policymakers and clinicians in making effective clinical decisions [ 2 ]. However, the application of AI technology to disease detection, cancer patient screening, therapy selection, reducing medication errors, and productivity improvement is now growing [ 3 , 4 , 5 , 6 ].…”

Applications of Artificial Intelligence Technologies in COVID-19 Research: A Bibliometric Study

“…To the best of our knowledge, this is the first one to evaluate the contemporary COVID-19 literature than emphasized on the applications of AI technologies. In recent years, scholars and practitioners are increasingly using AI in health sciences research that is facilitating the use of advanced analytics to better understand different aspects of human health and wellbeing [11,13]. However, the number of articles and limited research domains within the same suggests that the application of AI in COVID-19 research is a developing area in the global research landscape.…”

“…Scholarly contributions from diverse fields of knowledge such as computer sciences, mathematics, biology, and psychological sciences have informed the development of AI, which is widely used in modeling, replicating, understanding, and predicting complex problems, processes, and outcomes in health sciences [9,10]. The use of AI in modern medicine is well documented in several metaanalyses in different clinical sub-specialties [11][12][13]. In the era of COVID-19, a growing interest in the use of AI developed in the global health community.…”

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning

“…Artificial intelligence has recently attracted much attention in various fields of health and medicine. Different artificial intelligence and machine learning (ML) methods have been applied for various purposes, including image recognition, patient phenotyping, and outcome prediction for diseases such as cancer [15][16][17][18][19], cardiac arrest [20], Alzheimer's disease [21][22][23], respiratory diseases [24,25], rheumatic diseases [26], cornea and retinal diseases [27,28], gastrointestinal diseases [29,30], and infectious diseases [31][32][33][34][35]. These studies revealed that artificial intelligence has the capacity to assist clinicians in the disease diagnosis with high efficiency and accuracy.…”