Search Results (6)

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine. Full article

The American Academy of Ophthalmology and the National Heart, Lung and Blood Institute recommend patients with sickle cell disease (SCD) undergo dilated funduscopic exams (DFE) every 1–2 years to screen for sickle retinopathy. There is a paucity of data on the adherence rate to these guidelines; a retrospective study was performed to evaluate our institution’s adherence. A chart review of 842 adults with SCD, seen 3/2017–3/2021 in the Montefiore healthcare system (All Patients), was done. Only about half of All Patients (n = 842) had >1 DFE during the study period (Total Examined Patients, n = 415). The Total Examined Patients were categorized as screening, those without retinopathy (Retinopathy−, n = 199), or follow-up, including individuals previously diagnosed with retinopathy (Retinopathy+, n = 216). Only 40.3% of screening patients (n = 87) had DFE at least biennially. As expected, there was a significant decrease in the average DFE rate of the Total Examined Patients after the COVID-19 pandemic started (13.6%) compared to pre-COVID (29.8%, p < 0.001). Similarly, there was a significant decrease in the screening rate of Retinopathy− patients from 18.6% on average pre-COVID to 6.7% during COVID (p < 0.001). This data shows the sickle retinopathy screening rate is low and innovative approaches may need to be employed to remedy this issue. Full article

(1) Background: Retinal vascular imaging plays an essential role in diagnosing and managing chronic diseases such as diabetic retinopathy, sickle cell retinopathy, and systemic hypertension. Previously, we have shown that individuals with pulmonary arterial hypertension (PAH), a rare disorder, exhibit unique retinal vascular changes as seen using fluorescein angiography (FA) and that these changes correlate with PAH severity. This study aimed to determine if color fundus (CF) imaging could garner identical retinal information as previously seen using FA images in individuals with PAH. (2) Methods: VESGEN, computer software which provides detailed vascular patterns, was used to compare manual segmentations of FA to CF imaging in PAH subjects (n = 9) followed by deep learning (DL) processing of CF imaging to increase the speed of analysis and facilitate a noninvasive clinical translation. (3) Results: When manual segmentation of FA and CF images were compared using VESGEN analysis, both showed identical tortuosity and vessel area density measures. This remained true even when separating images based on arterial trees only. However, this was not observed with microvessels. DL segmentation when compared to manual segmentation of CF images showed similarities in vascular structure as defined by fractal dimension. Similarities were lost for tortuosity and vessel area density when comparing manual CF imaging to DL imaging. (4) Conclusions: Noninvasive imaging such as CF can be used with VESGEN to provide an accurate and safe assessment of retinal vascular changes in individuals with PAH. In addition to providing insight into possible future clinical translational use. Full article

Sickle cell retinopathy (SCR) is classified by Goldberg based on peripheral vascular changes. Ultra-wide field (UWF) imaging has enhanced visualization of the peripheral retina. However, there is no consensus on the optimal imaging technique for the screening of SCR. We performed a monocentric observational cross-sectional study to compare UWF fundus photography (UWF-FP) with UWF angiography (UWF-FA). All patients who underwent UWF-imaging (Optos, PLC, Scotland, UK) for screening of sickle cell retinopathy between January 2016 and December 2019 were retrospectively included. Eyes with previous laser treatment or concomitant retinal disease were excluded. UWF-FP images were graded based on the Goldberg classification by four graders with various degrees of experience. UWF-FA pictures were reviewed by an independent retina specialist. Differences in Goldberg staging across UWF-FP and UWF-FA were assessed. A total of 84 eyes of 44 patients were included. Based on UWF-FA, most eyes were stage 2 (77.4%) and 19 were stage 3 (22.6%). The pre-retinal neovascularization detection sensitivity on UWF-FP was 52.6 to 78.9%, depending on the graders. UWF-FA led to a later Goldberg stage of retinopathy, in most cases from stage 1 to stage 2. Neovascularization (stage 3) was not detected by our graders on UWF-FP in 21.1 to 57.9% of eyes. UWP-FP tends to underestimate Goldberg stages of retinopathy compared with UWF-FA and is less accurate when detecting neovascularization in sickle cell retinopathy, which has a direct impact on therapeutic management and prognosis. Full article

Purpose: To evaluate the sensitivity and specificity of ultrawide-field fundus photography (UWF-FP) for the detection and classification of sickle cell retinopathy (SCR) by ophthalmologists with varying degrees of expertise in retinal disease. Methods: Patients presenting with sickle cell disease (SCD) in the Créteil University Eye Clinic, having undergone UWF-FP and ultrawide-field fluorescein angiography (UWF-FA) on the same day, were retrospectively included. Eyes with previous retinal photocoagulation were excluded. SCR was graded independently by UWF-FP and UWF-FA using Goldberg classification by two ophthalmologists with varying expertise levels. Results: Sixty-six eyes of 33 patients were included in the study. The sensitivity of UWF-FP for the detection of proliferative SCR was 100%, (95% confidence interval [CI95%] 76.8–100) for the retinal specialist and 100% (CI95% 71.5–100) for the ophthalmology resident. The specificity of UWF-FP for the detection of proliferative SCR was 100% (CI95% 92.7–100) for the retinal specialist and 98.1% (CI95% 89.7–100) for the ophthalmology resident. Conclusions: UWF-FP is a valuable exam for proliferative SCR screening, with excellent sensitivity and specificity and a good inter-grader agreement for ophthalmologists with various degree of skills, and is easy to use in a real-life setting. Full article

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought (1) to differentiate normal from diseased ocular conditions, (2) to differentiate different ocular disease conditions from each other, and (3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine. Full article