Utility of Ward-Based Retinal Photography in Stroke Patients

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events: a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations. However, the ability of retinal vasculature changes, specifically focusing on retinal vessel diameter, to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively. While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events, they have not incorporated this information into a predictive model. Therefore, this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke. Additionally, we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors. We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University. All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years. We found that, after adjusting for related risk factors, patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin (MAD0.5-1.0DD) of ≥ 74.14 μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin (MVD0.5-1.0DD) of ≥ 83.91 μm tended to experience recurrent cerebrovascular events. We established three multivariate Cox proportional hazard regression models: model 1 included traditional risk factors, model 2 added MAD0.5-1.0DD to model 1, and model 3 added MVD0.5-1.0DD to model 1. Model 3 had the greatest potential to predict subsequent cerebrovascular events, followed by model 2, and finally model 1. These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke, and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Retinal blood flow in critical illness and systemic disease: a review

BACKGROUND

Assessment and maintenance of end-organ perfusion are key to resuscitation in critical illness, although there are limited direct methods or proxy measures to assess cerebral perfusion. Novel non-invasive methods of monitoring microcirculation in critically ill patients offer the potential for real-time updates to improve patient outcomes.

MAIN BODY

Parallel mechanisms autoregulate retinal and cerebral microcirculation to maintain blood flow to meet metabolic demands across a range of perfusion pressures. Cerebral blood flow (CBF) is reduced and autoregulation impaired in sepsis, but current methods to image CBF do not reproducibly assess the microcirculation. Peripheral microcirculatory blood flow may be imaged in sublingual and conjunctival mucosa and is impaired in sepsis. Retinal microcirculation can be directly imaged by optical coherence tomography angiography (OCTA) during perfusion-deficit states such as sepsis, and other systemic haemodynamic disturbances such as acute coronary syndrome, and systemic inflammatory conditions such as inflammatory bowel disease.

CONCLUSION

Monitoring microcirculatory flow offers the potential to enhance monitoring in the care of critically ill patients, and imaging retinal blood flow during critical illness offers a potential biomarker for cerebral microcirculatory perfusion.

Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma

Purpose

To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (5) detection of referable glaucomatous optic neuropathy (GON).

Patients and Methods

Five algorithms were designed. Retrospective study from a database of 306,302 images, Optretina’s tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy. Main outcome measure was area under the curve (AUC) with accuracy, sensitivity and specificity.

Results

Determination of retinal fundus image had AUC of 0.979 with an accuracy of 96% (sensitivity 97.7%, specificity 92.4%). Determination of good quality retinal fundus image had AUC of 0.947, accuracy 91.8% (sensitivity 96.9%, specificity 81.8%). Algorithm for OD/OS had AUC 0.989, accuracy 97.4%. AMD had AUC of 0.936, accuracy 86.3% (sensitivity 90.2% specificity 82.5%), GON had AUC of 0.863, accuracy 80.2% (sensitivity 76.8%, specificity 83.8%).

Conclusion

Deep learning algorithms can differentiate a retinal fundus image from other images. Algorithms can evaluate the quality of an image, discriminate between right or left eye and detect the presence of AMD and GON with a high level of accuracy, sensitivity and specificity.