Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.

Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granu la ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Purpose: The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). Methods: We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations. Results: Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier. Conclusions: Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps. Translational Relevance: This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

Assessment of prevalence and need for screening of diabetic retinopathy using non-mydriatic fundus camera in rural and tribal diabetic populations in Maharashtra.

PURPOSE: To assess the prevalence of DR and the need for screening and management of DR with medical management of diabetes in rural and tribal population in Maharashtra. METHODS: The known diabetics of rural area and tribal area were screened at corresponding primary health centers, subcenters, and village level with the help of local healthcare workers using a portable non-mydriatic fundus camera. The prevalence of blindness among known diabetics in rural area was 1.29%, and 0.84% in tribal area. RESULTS: In the rural area, the prevalence of diabetic retinopathy (DR) was 5.67% ( n = 776), out of those 18.18% had sight threatening diabetic retinopathy (STDR). The prevalence of DR was 7.73% ( n = 711) in tribal areas, out of those, 30.90% had STDR. CONCLUSIONS: The significant risk factors were identified to be the duration of diabetes and poor glycemic control. Implementation of targeted interventions for screening and management are required to reduce the risk of blindness among known diabetics in rural and tribal areas.

Assessment of Microvascular Changes After Rhegmatogenous Retinal Detachment Repair Using Wide-Field Swept-Source Optical Coherence Tomography Angiography.

BACKGROUND AND OBJECTIVE: Our objective was to evaluate retinal microvascular changes and visual outcomes following rhegmatogenous retinal detachment (RRD) repair using wide-field swept-source optical coherence tomography angiography (WF SS-OCTA). PATIENTS AND METHODS: The study included 116 eyes of 111 patients with macula-off (n = 68) or macula-on (n = 48) RRD treated with a single successful procedure, 79 fellow eyes, and 183 eyes of control patients imaged with WF SS-OCTA (3 ×3, 6 ×6, and 12 ×12 mm images). Mixed-effects multiple linear regression models were used for statistical analysis. RESULTS: Vessel density (VD) and vessel skeletonized density (VSD) of the superficial capillary plexus (3 ×3 mm scans) and full-thickness retina (12 ×12 mm) were significantly reduced in RRD eyes compared to fellow and control eyes. Decreased VSD and VD in all layers (3 ×3 mm and 6 ×6 mm) were significantly associated with greater preoperative extent of retinal detachment (P < 0.05) and poorer postoperative best-corrected visual acuity (BCVA) in RRD eyes (P < 0.05). Macula-off status was associated with increased foveal avascular zone irregularity (12 ×12 mm, P = 0.02). CONCLUSIONS: Decreased VD on WF SS-OCTA is associated with poorer postoperative BCVA following RRD repair. [Ophthalmic Surg Lasers Imaging Retina 2024;55:310-317.].

Retinal oximetry and microvascular assessment after hypertensive pregnancy complications.

PURPOSE: Women with hypertensive disorders of pregnancy (HDP) are at increased risk of developing premature cardiovascular disease (CVD). The mechanisms behind this are not fully understood, but microvascular alterations have been documented in retinal arterioles and venules. The aim of this study was to use non-invasive retinal imaging to investigate the structural and functional properties of arterioles, venules and capillaries in this patient group. METHODS: We examined 27 women with previous HDP and 23 controls at 3 years postpartum. The retinal microvasculature was assessed by vessel calibre measurements, retinal oximetry and optical coherence tomography angiography. Differences were analysed using non-parametric tests and multiple regression analyses, adjusted for age and body mass index. RESULTS: Median arteriolar oxygen saturation (SaO2; 94.2% vs. 93.0%), venular oxygen saturation (SvO2; 60.1% vs. 62.4%) and arteriovenous saturation difference (AV-difference; 32.8% vs. 32.3%) were similar across groups. Capillary vessel density (VD; 46.2% vs. 46.3%), skeletonised VD (VSD; 21.3 vs. 21.1 mm/mm2) and vessel diameter index (21.65 vs. 21.86) were also comparable. In the HDP group, mean arterial pressure (MAP) was positively correlated with AV-difference (R2 = 0.209) and negatively correlated with arteriolar diameter (CRAE; r2 = 0.382). CONCLUSIONS: Structural microvascular alterations appear not to be key biomarkers for CVD risk after HDP as early as 3 years postpartum in otherwise healthy women. Further studies are needed to evaluate whether such changes occur later in life. MAP was associated with AV-difference only in the HDP group, suggesting specific mechanisms affecting functional microvascular properties in these women.

A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data.

PURPOSE: To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. METHODS: The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed. RESULTS: 2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. CONCLUSIONS AND RELEVANCE: Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.

Retinografia como forma de rastreio de retinopatia diabética em hospital terciário do Sistema Único de Saúde / Color retinography as a means of screening for diabetic retinopathy in the tertiary hospital of Unified Health System

RESUMO Objetivo Avaliar o impacto da triagem de retinopatia diabética de paciente diabéticos realizada com retinografia colorida. Métodos Estudo retrospectivo, de caráter descritivo, avaliando laudos de retinografias realizadas desde a implementação do protocolo da triagem de retinopatia diabética de paciente diabéticos acompanhados no Ambulatório de Endocrinologia de um hospital terciário do Sistema Único de Saúde, de maio de 2018 até maio de 2020. Resultados Realizaram retinografia 727 pacientes diabéticos, que tinham entre 14 e 91 anos, sendo a maioria com 60 anos ou mais (53,2%), do sexo feminino (68%) e brancos (87,6%). Não apresentavam retinopatia diabética 467 (64,2%) pacientes, 125 (17,2%) tinham retinopatia diabética não proliferativa, 37 (5,1%) retinopatia diabética não proliferativa grave e/ou suspeita de edema macular, 65 (8,9%) retinopatia diabética proliferativa, 21 (2,9%) suspeita de outras patologias, e as imagens de 12 (1,7%) pacientes eram insatisfatórias. Foram considerados de alto risco (aqueles com retinopatia diabética não proliferativa grave e/ou edema macular, retinopatia diabética proliferativa ou imagem insatisfatória) 114 (15,68%) pacientes. Conclusão O rastreio de retinopatia diabética com retinografia colorida possibilitou a detecção de pacientes diabéticos de alto risco que necessitavam atendimento com brevidade, permitindo o acesso deles à consulta oftalmológica e diminuindo a morbidade da doença relacionada ao tratamento tardio. Os demais foram encaminhados à Atenção Primária para regulamentação, por meio do Sistema de Regulação.

ABSTRACT Objective To evaluate the impact of diabetic retinopathy (DR) screening using color retinography in diabetic patients. Methods Retrospective descriptive study, evaluating reports of all retinographs performed since the implementation of the protocol for screening for diabetic retinopathy in diabetic patients followed up at the endocrinology outpatient clinic of a tertiary hospital of the Unified Health System, from May 2018 to May 2020. Results 727 diabetic with age range from 14 to 91 years old, the majority being 60 years old or older (53.2%), female (68%) and white (87.6%), patients underwent retinography. Of the patients, 467 (64.2%) did not have DR, 125 (17.2%) had non-proliferative DR, 37 (5.1%) had severe non-proliferative DR and/or suspected macular edema, 65 (8.9%) had proliferative DR, 21 (2.9%) had suspicion signs of other pathologies and 12 (1.7%) had unsatisfactory images. A total of 114 (15.68%) patients were considered at high risk (those with severe non-proliferative NP and/or EM, proliferative DR or poor image) and were referred for comprehensive ophthalmic evaluation. Conclusion The screening of RD with color retinography enabled the detection of high-risk diabetic patients who needed assistance sooner and enabled their access to ophthalmologic consultation, which decreased disease morbidity. The others were referred to primary care for regulation through the Regulation System (SISREG).