diagnostics Review Automated Systems for Calculating Arteriovenous Ratio in Retinographies: A Scoping Review Rosa García-Sierra 1,2,3,4 , Victor M. López-Lifante 1,5, * , Erik Isusquiza Garcia 6 , Antonio Heras 1,7 , Idoia Besada 6 , David Verde Lopez 8 , Maria Teresa Alzamora 1,7 , Rosa Forés 1 , Pilar Montero-Alia 1 , Jurgi Ugarte Anduaga 6 and Pere Torán-Monserrat 1,2,4,9 1 Research Support Unit Metropolitana Nord, Primary Care Research Institut Jordi Gol (IDIAPJGol), 08303 Mataró, Spain 2 Multidisciplinary Research Group in Health and Society GREMSAS (2017 SGR 917), 08007 Barcelona, Spain 3 Nursing Department, Faculty of Medicine, Universitat Autònoma de Barcelona, Campus Bellaterra, 08193 Barcelona, Spain 4 Primary Care Group, Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain 5 Palau-solità i Plegamans Primary Healthcare Centre, Palau-solità i Plegamans, Gerència d’Àmbit d’Atenció Primària Metropolitana Nord, Institut Català de la Salut, 08184 Barcelona, Spain 6 ULMA Medical Technologies, S. Coop, 20560 Onati, Spain 7 Primary Healthcare Centre Riu Nord-Riu Sud, Gerència d’Àmbit d’Atenció Primària Metropolitana Nord, Institut Català de la Salut, Santa Coloma de Gramenet, 08921 Barcelona, Spain 8 Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), 08007 Barcelona, Spain 9 Department of Medicine, Faculty of Medicine, Universitat de Girona, 17004 Girona, Spain * Correspondence: vmlopezli.mn.ics@gencat.cat Citation: García-Sierra, R.; López-Lifante, V.M.; Isusquiza Abstract: There is evidence of an association between hypertension and retinal arteriolar narrowing. Garcia, E.; Heras, A.; Besada, I.; Verde Manual measurement of retinal vessels comes with additional variability, which can be eliminated Lopez, D.; Alzamora, M.T.; Forés, R.; using automated software. This scoping review aims to summarize research on automated retinal Montero-Alia, P.; Ugarte Anduaga, J.; et al. Automated Systems for vessel analysis systems. Searches were performed on Medline, Scopus, and Cochrane to find studies Calculating Arteriovenous Ratio in examining automated systems for the diagnosis of retinal vascular alterations caused by hyperten- Retinographies: A Scoping Review. sion using the following keywords: diagnosis; diagnostic screening programs; image processing, Diagnostics 2022, 12, 2865. computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hy- https://doi.org/10.3390/ pertension; retinal vessels; arteriovenous ratio and retinal image analysis. The searches generated diagnostics12112865 433 articles. Of these, 25 articles published from 2010 to 2022 were included in the review. The Academic Editors: Mario Damiano retinographies analyzed were extracted from international databases and real scenarios. Automated Toro, Daniele Tognetto and Rosa systems to detect alterations in the retinal vasculature are being introduced into clinical practice for Giglio diagnosis in ophthalmology and other medical specialties due to the association of such changes with various diseases. These systems make the classification of hypertensive retinopathy and cardiovascu- Received: 2 October 2022 lar risk more reliable. They also make it possible for diagnosis to be performed in primary care, thus Accepted: 14 November 2022 optimizing ophthalmological visits. Published: 18 November 2022 Publisher’s Note: MDPI stays neutral Keywords: image processing; computer assisted; retinal vessels; retinal image analysis; diagnosis; with regard to jurisdictional claims in hypertensive retinopathy; diagnostic screening programs published maps and institutional affil- iations. 1. Introduction Copyright: © 2022 by the authors. Fundus photography, also known as retinography, is a popular imaging technique Licensee MDPI, Basel, Switzerland. used to visualize changes in the retinal vessels through the pupil. It can capture changes in This article is an open access article vascular caliber and the global geometric patterns of the retina [1]. It is also able to detect distributed under the terms and signs of retinopathy—such as microaneurysms, hemorrhages, cotton wool spots and hard conditions of the Creative Commons exudates, and symptoms of the retinal arteriolar wall (e.g., generalized and focal arteriolar Attribution (CC BY) license (https:// narrowing and arteriovenous nicking)—all of which are often observed in patients with creativecommons.org/licenses/by/ systemic diseases, such as diabetes and hypertension. 4.0/). Diagnostics 2022, 12, 2865. https://doi.org/10.3390/diagnostics12112865 https://www.mdpi.com/journal/diagnostics Diagnostics 2022, 12, 2865 2 of 14 Regarding diabetes, the 1989 Saint Vincent Declaration set the goal of reducing diabetes-related blindness by one-third over the next five years. This was restated in the 2005 Liverpool Declaration’s objective of establishing systematic screening programs to reach at least 80% of the population with diabetes by 2021 [2]. The increased demand for diabetic retinopathy (DR) screening resulting from systematic programs could be met using automated retinal image analysis systems. Such systems can be used in different DR screening scenarios and offer relatively high sensitivity and a substantial reduction in the workload of the health system. Moreover, they are now mature enough to be safely used in DR screening [3,4]. Automated tools improve the quality of DR screening and accessibility to medical care while reducing the cost of the disease by promoting early detection and treatment, which is essential to stop progression [5]. Regarding hypertension, there is evidence that it is associated with retinal arteri- olar narrowing. Retinal vessel diameter is expressed as arteriovenous ratio (AVR). Ac- cording to the Keith–Wagener–Barker classification, AVR values of less than 0.66 reflect hypertensive retinopathy [6]. Arteriolar narrowing is associated with more severe coronary heart disease, stroke, and mortality [6–9]. There has also been a recent increase in evidence showing that retinal arteriolar narrowing, retinal venular widening, and a suboptimal retinal vascular network are associated with poorer cognitive performance [10–12]. Retinal imaging techniques provide unique information about the state of the microvasculature and neuronal structure, different from current neuroimaging markers, such as brain magnetic resonance imaging (MRI), and systemic markers, such as blood pressure. While retinal imaging cannot fully replace PET scans or MRIs in the diagnosis of disease, it does offer a complementary approach to these brain imaging techniques and has considerable potential in clinical and research settings [13]. For all these reasons, retinal imaging can be used as a risk stratification tool because studies suggest that the addition of retinal measures improves the prediction of stroke (an improvement on established risk factors of approximately 10%) [14,15]. Although just a modest improvement in prediction, these findings suggest that adding a combination of various retinal features and/or retinal functional parameters (i.e., a “multimarker ap- proach”) may further improve the prediction of dementia and stroke. It might also enable the identification of a more specific subgroup of patients who could benefit from more intensive and expensive examinations, such as brain MRI. Retinal vascular imaging has also been used to examine the effects of antihypertensive therapy, showing that lowering blood pressure leads to the regression of retinal vascular signs [16,17]. While there have been no significant intervention studies using changes in retinal images as alternative outcome measurements in dementia and stroke, this approach has substantial potential. In addition to its clinical value, retinal imaging may also be a worthwhile research tool in major brain and neurological diseases, such as multiple sclerosis [18–20], depression [21,22], and schizophrenia [23,24]. Evaluating retinographies manually implies additional variability in retinal vessel measurements, even when following a standardized protocol. This variability is eliminated if fully automated software is used to measure retinal vascular caliber and other anomalies, although there may be other additional sources of variation in the measurements, such as retinal pigmentation, pupil dilation, the presence of cataracts and other media opacities, photographic technique, type of camera (mydriatic/non-mydriatic or desktop/portable), and image quality (brightness, focus, and contrast) [25]. Manually segmenting vessels, labeling arteries and veins, and localizing the optic disc is a time-consuming task that decreases process efficiency. However, over the past two decades, multiple software systems have been developed to measure and semi-automatically assess the retinal vessel caliber from fundus photographs using artificial intelligence (AI) algorithms [26]. Research question: What is the current stage of implementation of automated retinal vessel analysis systems retinographies? Diagnostics 2022, 12, 2865 3 of 14 Aim: This scoping review aims to summarize the research available on automated retinal vessel measurement systems so they may be considered in future research and introduced into clinical practice. 2. Materials and Methods This review followed the PRISMA extension checklist for scoping reviews [27]. 2.1. Search Strategy, Data Sources, and Selection Searches were performed in the Medline, Scopus, and Cochrane electronic databases to locate studies published between 1 january 2004 and 1 september 2022 examining au- tomated systems for the diagnosis of retinal vessel alterations caused by hypertension. The following keywords were used: diagnosis; diagnostic screening programs; image pro- cessing, computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hypertension; retinal vessels; arteriovenous ratio (no MeSH); retinal image analysis (no MeSH). 2.2. Selection Criteria Articles were included in the review if they met the following inclusion criteria: (1) Automated systems were used to partially or totally analyze photographic images of the retina. (2) Changes in the retinal vascular network and/or retinal vascular measurements were analyzed. (3) The publication was peer-reviewed. (4) The study was observational, descriptive (population, cross-sectional), analytical (case studies and controls, cohorts), experimental (clinical trials), or a validation of experiments/new image analysis methods. Only papers written in English were selected. Studies using automated systems for diabetes screening were excluded. Qualitative studies and gray literature were excluded. 2.3. Selection of Studies Abstracts and articles were independently reviewed by two members of the research team based on predetermined inclusion and exclusion criteria. When it was unclear whether an article should be included or some discrepancy appeared, the coordinating researcher of the study also reviewed it. 2.4. Data Extraction Initial data extraction elements included: author, country, research aim, study de- sign, study setting, interpretation system, degree of software automation (semi-automatic, automatic), lesions that the system was able to detect, focus of the photograph and area analyzed, sensitivity, specificity, diagnostic precision, economic evaluation, time savings, local management or the possibility of electronically sending the image to a repository, possibility of comparison for patient follow-up. Data from each article were independently extracted by two of the authors and then verified by two others. 3. Results 3.1. Search Process The PRISMA flow diagram (Figure 1) describes the steps taken to select the articles [28]. The search strategies generated 433 articles, of which 58 full-length articles were evaluated for eligibility. Of these, 25 articles were included in the scoping review. The PRISMA flow diagram (Figure 1) describes the steps taken to select the article [28]. The search strategies generated 433 articles, of which 58 full-length articles were eva Diagnostics uated for eligibility. Of these, 25 articles were included in the scoping review.4 of 14 2022, 12, 2865 Figure 1. PRISMA 2020 flow diagram. 3.2. Characteristics of the Articles Figure 1. PRISMA 2020 flow diagram. The articles included in the review were published between 2010 and 2022, and the number of retinographies analyzed ranged from 20 to 95,716. These retinographies were 3.2. Characteristics of thebyArticles provided international databases, as well as real scenarios. Table 1 presents a summary of the characteristics of the studies, including first author, year of publication, the country The articles included where the studyin the was reviewstudy conducted, wereaim,published sample, numberbetween 2010 and of retinographies, name2022, of the and th number of retinographies software used, analyzed rangedand, degree of automation, from 20the lastly, toscenario 95,716.in These which it retinographies was tested. wer The median number of retinographies analyzed in the included provided by international databases, as well as real scenarios. Table 1 presents a summar studies was 180, with a maximum of 54,714 and a minimum of 40. In relation to the degree of automation, in of the characteristics of they 10 articles the used studies, including automatic firstinauthor, software and year of systems. 15 semi-automated publication, the countr where the study was conducted, study aim, sample, number of retinographies, name o 3.3. Interpretation Procedures the software used, degree of automation, and, lastly, the scenario in which it was tested The analysis systems included in these articles detect various alterations of the vessels, The median number including of retinographies tortuosity, analyzed arteriolar and venular in the caliber, and even included studies AVR calculation. Table 2was shows180, wit a description of the retinal lesions detected in each study, the focus a maximum of 54,714 and a minimum of 40. In relation to the degree of automation, in 1 of the photograph, and the area of the retina that was analyzed. The focus of the image was mainly on the macula articles they used andautomatic optic disc, andsoftware the analyzed and areain 15 semi-automated ranged systems. between 2 to 3 radii from the optic disc. 3.3. Interpretation Procedures The analysis systems included in these articles detect various alterations of the ves sels, including tortuosity, arteriolar and venular caliber, and even AVR calculation. Tabl Diagnostics 2022, 12, 2865 5 of 14 Table 1. Characteristics of the studies. Author, Year Country Aim Sample Photographs Software Automation Type Scenario Measure arteriovenous relationship Pakistan and United and degree of retinal vessel Badawi, 2022 [29] 504 504 VAMPIRE a Automated RVM dataset b Arab Emirates tortuosity to detect and classify hypertensive retinopathy. Assess the association between cumulative blood pressure, Huang, 2022 [30] China 1818 Semi-automated ARIC c averaged over 25 years, and retinal vessel calibers. Method for the differentiation and classification of retinal vessels by Real scenario Irshad, 2021 [31] Australia 142 BPSO Automated Binary Particle Swarm Optimization INSPIRE-AVR VICAVR (BPSO). Report on the construction of a model to further explore the CNN 8 DRIVE 1 Dai, 2020 [32] China 1419 2012 Automated pathophysiological changes of the architecture STARE 2 retinal microvasculature. Assess the relevance of a retinal Maderuelo-Fernandez, Spain vessel analysis system in target 250 495 ALTAIR d Semi-automated Real scenario 2020 [26] organ damage and vascular risk. Evaluate whether retinal vessel Robertson, UK measurements were associated 440 880 VAMPIRE a Semi-automated NICOLA 3 2020 [33] with hypertension. Examine the association between United Kingdom Tapp, 2019 [34] UK retinal vessel morphometry and 54,714 95,716 QUARTZ e Automated Biobank blood pressure and arterial stiffness. Determine whether a high burden of white matter hyperintensities can be CU-RISK Lau, 2019 [35] Hong Kong 180 180 ARIA f Automated detected through images of COHORT 4 the retina. Examine the association between blood pressure measurements and Real scenario He, 2018 [36] China 1501 1501 IVAN g Semi-automated changes in the retinal Pediatrics microvasculature. Assess retinal vessel diameter as a Adiarti, 2018 [37] Indonesia 54 108 SIVA h Semi-automated Real scenario marker of glaucoma. Assess AVR calculation as a classifier INSPIRE-AVR 5 Akbar, 2018 [38] Pakistan of the degree of arterial 100 198 - Automated VICAVRAVRDB 6 hypertension. Diagnostics 2022, 12, 2865 6 of 14 Table 1. Cont. Author, Year Country Aim Sample Photographs Software Automation Type Scenario Compare a new method of retinal Iwase, 2017 [39] Japan vessel measurement with the 99 180 - Semi-automated Real scenario IVAN system. Describe the associations between Yip, 2017 [11] Singapore retinal vascular parameters and 1256 2512 SIVA h Semi-automated Real scenario chronic kidney disease. Investigate the association between poor glycemic control and Real scenario Li, 2017 [40] Singapore 55 110 SIVA jh Semi-automated subsequent changes in the retinal Pediatrics microvasculature. Evaluate the usefulness of Vázquez Spain measuring arteriovenous ratio to 768 2262 VesselMap2 Semi-automated Real scenario Dorrego, 2016 [41] detect silent brain ischemia. Cioran and Cavallari, Develop a semi-automated method USA, Italy 54 108 BRetina Semi-automated Real scenario 2015 [42] to assess retinal vessel morphology. plugins DRIVE 1 Present a fully automated software e STARE 2 Fraz, 2015 [43] Pakistan and UK - 16,000 QUARTZ Automated to analyze the retinal vasculature. INSPIRE 5 CHASE-DB1 DIARETDB1 7 Develop a semi-automated method DRIVE 1 Estrada, 2015 [44] USA to distinguish arteries from veins in 110 130 - Semi-automated INSPIRE 5 WIDE fundus images. Identify the association between baseline retinal vascular caliber and Moradi, 2014 [45] USA 84 25 IVAN g Semi-automated Real scenario visual outcome of patients with diabetic macular edema. Present an automated retinal vessel Franklin, 2014 [46] India 40 40 ANN 9 Automated DRIVE 1 segmentation technique. Develop an automatic approach for Dashtbozorg, DRIVE1 INSPIRE-AVR 5 Portugal the classification of arteries and - 130 - Semi-automated 2014 [47] VICAVR veins of the retinal vasculature. Propose a methodology for Vázquez, 2013 [48] Spain classifying arteries and veins in the - 100 - Semi-automated VICAVR-2 fundus vasculature. Propose an automated computational framework for retinal Huang, 2012 [49] China - 40 - Automated DRIVE 1 vascular network labeling and branch order analysis. Diagnostics 2022, 12, 2865 7 of 14 Table 1. Cont. Author, Year Country Aim Sample Photographs Software Automation Type Scenario Develop a generic framework for Ortega, 2010 [50] Spain 96 173 SIRIUS i Semi-automated Real scenario processing retinal images. Present a fast, efficient, and Villalobos-Castaldi, Mexico automatic algorithm to extract - 20 - Automated DRIVE 1 2010 [51] vessels from retinal images. VAMPIRE a : Vessel Assessment and Measurement Platform for Images of the REtina. RVM b: Retinal Vessel Morphometry. ARIC c: Atherosclerosis Risk in Communities. ALTAIR d: Automatic image anaLyzer to assess reTinAl vessel calIber. QUARTZ e: Quantitative Analysis of Retinal Vessel Topology and size. ARIA f: Automated Retinal Image Analyzer (an open-source software designed for automatic recognition and computation of retinal factors and parameters). IVAN g: Interactive Vessel Analysis, University of Wisconsin–Madison. SIVA h: Singapore “I” Vessel Assessment. SIRIUS i: System for the Integration of Retinal Images Understanding Services. DRIVE 1: Digital Retinal Images for Vessel Extraction. STARE 2: STructured Analysis of the Retina. NICOLA 3: Northern Ireland COhort for the Longitudinal study of Ageing. CU-RISK COHORT 4: The Chinese University of Hong Kong—Risk index for Subclinical Brain Lesions in Hong Kong. INSPIRE-AVR 5: Iowa Normative Set for Processing Images of the REtina-ArterioVenous Ratio. AVRDB 6: local dataset Annotated dataset for Vessel Segmentation and Calculation of Arteriovenous Ratio. DIARETDB1 7: Standard DIAbetic RETinopathy Database caliBration level 1. CNN 8: Convolutional Neural Networks. ANN 9: Artificial Neural Networks. BPSO: Binary Particle Swarm Optimization. Table 2. Lesions detected. Author, Year Measurements Focus of the Image Area Analyzed S* SP ** DP *** Conclusions 2 to 3 radii from the Hybrid tool that combines AVR and tortuosity to Badawi, AVR Tortuosity Optic disc optic disc 95.5% - 96.8% detect and grade the severity of 2022 [29] The entire retina hypertensive retinopathy. High blood pressure, averaged over 25 years, and Huang, 2 to 3 radii from the CRAE a CRVE b AVR Optic disc - - - specifically DBP, was associated with narrower 2022 [30] optic disc retinal vessel diameter. 92.7% Proposal of a method that offers improved retinal Irshad, Classification of arteries 2 to 3 radii from the Optic disc - - 94.6% vessel classification and is robust in 2021 [31] and veins optic disc 91.9% three different databases. Changes in retinal vessel branching pattern were 60.9% the most significant response to high blood Subclinical The entire retinal 70.5% Dai, 2020 [32] Macula 59.3% 63.8% pressure compared to other retinal microvascular morphological features vasculature AUC: biomarkers such as caliber, tortuosity, fractal 65.1% dimension, and branching angle. A concomitant association of retinal vessel Maderuelo-Fernandez, Three concentric circles CRAE a CRV b AVR Optic disc - - - measurements with other cardiovascular 2020 [26] around the optic disc parameters and cardiovascular risk is shown. Annular segment that Semi-automated AVR measurements on Nasal-annular 6.5 to 8.5 radii from the Robertson, 2020 [33] subtends 180◦ nasally to - - - ultra-widefield fundus images were associated AVR optic disc the optic disc with hypertension. Arteriolar and venular The entire retinal Associations between retinal vessel morphometry, Tapp, 2019 [34] Optic disc and macula - - - diameter and Tortuosity vasculature blood pressure, and arterial stiffness index. Diagnostics 2022, 12, 2865 8 of 14 Table 2. Cont. Author, Year Measurements Focus of the Image Area Analyzed S* SP ** DP *** Conclusions a CRAE CRVE b Automatic retinal image analysis can detect Arteriole occlusion 2 to 3 radii from the community-dwelling subjects who do not have Lau, 2019 [35] Macula 93% 98% - Hemorrhages optic disc dementia and who have a significant burden of Tortuosity white matter hyperintensities in their brains. Higher blood pressure was significantly associated He, 2018 [36] CRAE a CRVE b AVR Optic disc and macula - - - with narrower retinal arterioles in a population of 12-year-olds. Retinal arteriolar narrowing may represent subclinical microcirculatory changes associated Adiarti, 1 to 4 radii from the CRAE a CRVE b AVR Optic disc and macula - - - with the presence of a glaucomatous optic disc 2018 [37] optic disc even in the absence of increased intraocular pressure. Akbar, The entire retinal The system is reliable for clinical use in the CRAE a CRVE b AVR Optic disc 98.9% 98.6% 98.8% 2018 [38] vasculature detection and grading of hypertensive retinopathy. The method would be especially useful to Iwase, 2 to 3 radii from the CRAE a CRVE b AVR Optic disc - - - accurately measure retinal vessel caliber in a 2017 [39] optic disc myopic population. Retinal microvascular abnormalities may reflect CRAE a CRVE b 1 to 4 radii from the early subclinical damage to the renal Yip, 2017 [11] Optic disc - - - Tortuosity optic disc microvasculature that is later associated with the development of chronic kidney disease. Pediatric patients with Type 1 diabetes and poor CRAE a CRVE b 1 to 4 radii from the Li, 2017 [40] Optic disc and macula - - - glycemic control showed abnormal retinal Tortuosity optic disc morphology in the short term. Alteration of the retinal vasculature is associated Vázquez Dorrego, 2 and 3 radii from the AVR Optic disc and macula - - - with an increased risk of silent brain ischemia in 2016 [41] optic disc hypertensive patients. 87.5% (HR) AVR, tortuosity index, and mean fractal dimension AVR Tortuosity Mean 3.5 radii from the optic 68.8% (HR) Cavallari, 2015 [42] Optic disc 90.9% - were altered in HR and CADASIL subjects Fractal Dimension disc 54.5% (CADASIL) (CADASIL) compared to age- and sex-matched control subjects. The entire retinal Provides quantifiable measurements of retinal Fraz, 2015 [43] AVR Tortuosity Optic disc 75.5% 98.0% 95.3% vasculature vessel morphology. 91.0% 91.0% 90.9% Classification of arteries The entire retinal 93.0% 94.1% 93.5% The software outputs a graph representing the Estrada, 2015 [44] Optic disc and macula and veins vasculature 91.7% 91.7% 91.7% retinal vasculature. 91.5% 90.2% 90.9% Correlation between retinal venular caliber and visual outcome in patients with diabetic macular Moradi, CRAE a 2 and 3 radii from the Optic disc - - - edema treated with ranibizumab. A higher CRVE, 2014 [45] CRVE b optic disc but not CRAE, was correlated with an improvement in vision. Diagnostics 2022, 12, 2865 9 of 14 Table 2. Cont. Author, Year Measurements Focus of the Image Area Analyzed S* SP ** DP *** Conclusions The entire retinal This technique has proven to be an effective tool for Franklin, 2014 [46] Vessel segmentation Macula - - - vasculature blood vessel segmentation in retinal images. The software outputs a graph representing the Classification of arteries The entire retinal 91% 86% Dashtbozorg, 2014 [47] Optic disc - retinal vasculature. Each segment of the retina is and veins vasculature 90% 84% then classified as an artery or vein. Classification of arteries Various circumferences The best results were achieved with four separate Vázquez, 2013 [48] Optic disc - - 87.7% and veins around the optic disc circumferences with a value of 0.5 radii. A useful tool to extract morphological Huang, Skeleton of the retinal The entire retinal Optic disc - - - characteristics in pathological studies related to 2012 [49] vascular tree vasculature the retina. Ortega, Various circumferences Sirius implements a web-based solution to analyze, AVR Optic disc - - 99.2% 2010 [50] around the optic disc manage, and understand retinal images. Villalobos-Castaldi, The entire retinal Tool to obtain an automatic threshold value to Vessel segmentation Optic disc 96.5% 94.8% 97.6% 2010 [51] vasculature segment vessels. S * Maximum sensitivity. SP ** Maximum specificity. DP *** Diagnostic precision. CRAE a : Central Retinal Arteriolar Equivalent. CRVE b : Central Retinal Venular Equivalent. AVR: Arteriole–to–Venule Ratio. CADASIL: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy. HR: Hypertensive Retinopathy. Diagnostics 2022, 12, 2865 10 of 14 Lastly, the sensitivity, specificity, and diagnostic accuracy were recorded, although this information was only reported in seven articles. The researchers were initially interested in studying additional data, such as economic evaluation, time savings, local management or the possibility of electronically sending the image to a repository, and the possibility of comparison for patient follow-up. However, the articles selected did not provide this information; therefore, such data were not collected. 3.4. Summary of the Results Most of the articles reviewed fall into two main categories. The first includes publi- cations dealing with automated or semi-automated systems that measure retinal vessels as a diagnostic method for other pathological processes. Retinal vessel measurement can be useful for diagnosing pathologies related to cardiovascular risk [26,34,41] and hyper- tension [26,33,34,36], dementia and stroke [35], glaucoma [37], chronic kidney disease [11], glycemic control in children [40], myopia [39], and severity of diabetic macular edema [45]. The second category comprises articles aimed at developing automated retinal vessel measurement systems. These articles cover several levels of development: those that use systems limited to vessel segmentation [46,49,51]; those that include vessel labeling and creation of the vascular tree [31,42,45,47,52]; and those that calculate AVR in order to grade hypertensive retinopathy [43,44,49,53]. 4. Discussion This scoping review aimed to summarize the research available on automated retinal vessel analysis systems in order to determine where automated AVR calculation systems are currently at in terms of implementation. The results indicate that interest in developing technology that facilitates the analysis of the retinal microvascular network has increased over the past eight years. Publications from 2014 and earlier refer to experiments to test the algorithms developed. As of 2015, the software developed from these algorithms has been introduced into clinical practice, demonstrating advantages in real-life scenarios, even though it is not yet widely nor systematically employed. The results of this review confirm that automated AVR calculation systems have not just been introduced as a diagnostic tool for retinal vascular disorders in the field of ophthalmology, but they have been extensively applied in other medical specialties as an accessible and efficient diagnostic tool for other pathologies. Numerous findings indicate that retinal vascular caliber is associated with var- ious systemic diseases, such as hypertension, obesity, diabetes, chronic kidney disease, and stroke. Moreover, since AVR is associated with the development of cardiovascular disease, examining it in this way offers a non-invasive view into the systemic microvasculature. Hypertensive retinopathy is an indicator of damage to other target organs. However, it is difficult for ophthalmologists to study hypertensive retinopathy in the early stages. Therefore, further research should be conducted on computer-assisted diagnoses that use AVR calculation to automatically detect hypertensive retinopathy and grade it in its early stages [54]. The automation of processes is a prerequisite to improving the affordability, efficiency, and accessibility of these procedures [55] and reducing the high subjectivity of manually assessing AVR [56]. Deep learning methods arise to compute AVR. Convolutional Neural Networks (CNN, Atlanta, GA, USA) obtain a good approximation of AVR value by applying a sequence of spatial filters, subsampling, and non-linear operations. The clinical applications of artificial intelligence in automated AVR reading could cover a wide range of tasks, including automating hypertensive retinopathy screening, supporting treatment decision-making, assessing systemic vascular status and cardiovascu- lar mortality [57], prescribing medications and diagnostic tests, and creating prognostic models of different diseases [58] to provide more efficient, precise, and sensitive methods in the interpretation of clinical data. Nonetheless, using artificial intelligence to analyze retinal microvasculature does present some limitations. Firstly, the images used to validate and train the AI may not have Diagnostics 2022, 12, 2865 11 of 14 enough ethnic variability to provide high external validity because there must be enough, but not too many, images for the processes to be efficient. Secondly, the data should be restricted to those criteria of greatest prognostic relevance, maintaining maximum diagnostic accuracy and minimum algorithm complexity. Consequently, if these processes were automated, they could be implemented in primary care for use by trained health professionals and in rural settings, thus facilitating the classification of cardiovascular risk and reducing the need to refer cases for evaluation by an ophthalmologist. Overall, this would result in the optimization of available health resources. 5. Conclusions In recent years, there has been increased interest in developing technology that facili- tates the analysis of the retinal microvascular network. Software has been developed and is being introduced into clinical practice not just as a diagnostic tool in the field of ophthal- mology, but also in other medical specialties, because there is an established association between various diseases and retinal vessel alterations. If automated processes for retinal vessel measurement were implemented in primary care for use by trained health professionals, fewer cases would need evaluation by an ophthalmologist, thus optimizing the available health resources. Moreover, such processes improve the reliability of vasculature measurements, which, in turn, leads to better classification of hypertensive retinopathy by eliminating observer subjectivity and taking cardiovascular risk into account. Also, the more reliable the mea- surements, the better the early diagnosis of other pathologies, such as dementia and stroke. Further research on the evaluation and implementation of these technologies is needed to recommend their use. Author Contributions: Conceptualization, P.T.-M. and V.M.L.-L.; methodology, R.G.-S.; formal analysis, R.G.-S.; A.H.; E.I.G.; and V.M.L.-L.; investigation, D.V.L.; M.T.A.; R.F.; P.M.-A.; I.B.; and J.U.A.; writing—original draft preparation, R.G.-S. and V.M.L.-L.; writing—review and editing, R.G.-S. and A.H.; supervision, P.T.-M.; project administration, R.G.-S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References 1. Cheung, C.Y.; Ikram, M.K.; Sabanayagam, C.; Wong, T.Y. Retinal microvasculature as a model to study the manifestations of hypertension. Hypertension 2012, 60, 1094–1103. [CrossRef] [PubMed] 2. Screening Programmes by Geography—Retina International’s Diabetic Eye Disease Toolkit. 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