What is it about?
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.
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Why is it important?
Visual impairment is a public health concern that has a negative impact on physical and mental health [1]. Visual impairment is associated with a high risk of chronic health conditions, including death. The prevalence and economic burden of visual impairment are exponentially increasing with an increasing number of aging populations [2]. It is estimated that the number of people with visual impairment will double by 2050 [3]. Several potential factors, such as cataract, age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, are responsible for an increased risk of blindness [4,5]. This highlights the important public health burden that visual impairment and blindness place on our health care system. Therefore, the early detection and quantitative diagnosis of retinal diseases can help to develop more preventive measures, thereby reducing the number of newly diagnosed cases and the associated financial burden.Herein, we report the results of a comprehensive systematic review of DL algorithms studies that investigated the performance of DL algorithms for retinal vessel segmentation in digital fundus photographs. Our primary objective was to precisely gauge the performance of DL methods for retinal vessel segmentation from color fundus images. The evaluation of DL performance can help policymakers to understand how DL could be a clinically effective tool for segmenting retinal vessels in under-resourced areas with a severe shortage of experts and infrastructure.
Perspectives
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.
Md.Mohaimenul Islam
Taipei Medical University
Read the Original
This page is a summary of: Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation, Journal of Clinical Medicine, April 2020, MDPI AG,
DOI: 10.3390/jcm9041018.
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