Optical coherence tomography angiography in Alzheimer's disease: a systematic review and meta-analysis

Alzheimer's disease pathophysiology in the Retina

The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography

BACKGROUND

To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).

METHODS

In this cross-sectional observational study, clinical data and OCTA parameters from 203 diabetic patients (203 eye) were used to establish the ML models, and those from 169 diabetic patients (169 eye) were used for independent external validation. The random forest, gradient boosting machine (GBM), deep learning and logistic regression algorithms were used to identify the presence of DR, referable DR (RDR) and vision-threatening DR (VTDR). Four different variable patterns based on clinical data and OCTA variables were examined. The algorithms' performance were evaluated using receiver operating characteristic curves and the area under the curve (AUC) was used to assess predictive accuracy.

RESULTS

The random forest algorithm on OCTA+clinical data-based variables and OCTA+non-laboratory factor-based variables provided the higher AUC values for DR, RDR and VTDR. The GBM algorithm produced similar results, albeit with slightly lower AUC values. Leading predictors of DR status included vessel density, retinal thickness and GCC thickness, as well as the body mass index, waist-to-hip ratio and glucose-lowering treatment.

CONCLUSIONS

ML-based multiclass DR classification using OCTA and clinical data can provide reliable assistance for screening, referral, and management DR populations.

Changes in the retina and choroid in patients with internal carotid artery stenosis: a systematic review and meta-analysis

Background

Internal carotid artery stenosis (ICAS) is a prevalent vascular condition associated with ischemic cerebrovascular disease. The ophthalmic artery is the first branch of the internal carotid artery stenosis (ICA). Given the crucial role of the ICA in ocular perfusion, we aimed to assess the thickness and vessel density of the retina and choroid in individuals with ICAS.

Methods

The PubMed and Embase databases were searched from inception to 10 January 2023 for studies evaluating retinal and choroidal changes between ICAS patients and healthy controls using optical coherence tomography (OCT) or optical coherence tomography angiography (OCTA). Data of interest were extracted and analyzed using Stata software version 16.

Results

Thirteen studies involving 419 ICAS eyes and 398 healthy eyes were included. The pooled results demonstrated that the average thickness of peripapillary retinal nerve fiber layer (pRNFL) (WMD = -0.26, 95% CI: -0.45 to -0.08, P = 0.005), ganglion cell complex (GCC) (WMD = -0.36, 95% CI: -0.65 to -0.06, P = 0.017), and choroid (WMD = -1.06, 95% CI: -1.59 to -0.52, P = 0.000), were significantly thinner in patients with ICAS than in healthy controls. The overall vessel density of the radial peripapillary capillaries (RPC) in whole-image scans was lower in ICAS patients than in healthy control subjects (WMD = -0.94, 95% CI: -1.49 to -0.39, P = 0.001). No differences were detected in the vessel density of the superficial capillary plexus (SCP) (WMD = -0.84, 95% CI: -1.15 to -0.53, P = 0.092), the deep capillary plexus (DCP) (WMD = -0.27, 95% CI: -0.56 to 0.03, P = 0.074), or the choriocapillaris (CC) (WMD = -0.39, 95% CI: -1.12 to 0.35, P = 0.300).

Conclusion

This systematic review and meta-analysis demonstrated that ICAS can reduce the vessel density of the RPC and the thickness of the retina and choroid. The retinal and choroidal microvasculature is a potential biomarker of the initial signal of ICAS.

Systematic review registration

https://inplasy.com/, identifier NPLASY202410038.

Optical Coherence Tomography Angiography Changes in Patients Diagnosed With Acute Coronary Syndrome: A Systematic Review and Meta-Analysis

We conducted a systematic review and meta-analysis to assess the association between optical coherence tomography angiography (OCTA) parameters and acute coronary syndrome (ACS). Two independent reviewers searched the electronic databases (MEDLINE (Medical Literature Analysis and Retrieval System Online), Scopus, Embase (Excerpta Medica Database), Cochrane Library, ClinicalTrials.gov, and World Health Organization International Clinical Trials Registry Platform) from inception until April 2023. According to the inclusion criteria of this review, eligible were observational studies, randomized control trials, and registry/database studies that included the eyes of adult ACS patients and assessed OCTA parameters within the macula. The pooled standardized mean differences (SMD) between patients diagnosed with ACS and healthy controls with a confidence interval (CI) of 95% were calculated using the Hartung-Knapp-Sidik-Jonkman random-effects method. The heterogeneity was assessed by I2 and the Cochran Q and a random effects model was applied. Seven studies were eligible and included in our systematic review (n = 898), of which three were included in the meta-analysis (n = 341). The pooled SMD in the superficial vascular plexus (SVP), deep vascular plexus (DVP), and foveal avascular zone (FAZ) were -0.46 (95% CI: -0.94 to 0.01, p = 0.05, I2 = 0%, three studies), -0.10 (95% CI: -3.20 to 3.00, p = 0.75, I2 = 67%, two studies), and 0.43 (95% CI: -1.22 to 2.09, p = 0.38, I2 = 92%, three studies), respectively. Our findings suggest that there are no differences in OCTA metrics between ACS patients and healthy individuals.

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

Potential Retinal Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) represents a major diagnostic challenge, as early detection is crucial for effective intervention. This review examines the diagnostic challenges facing current AD evaluations and explores the emerging field of retinal alterations as early indicators. Recognizing the potential of the retina as a noninvasive window to the brain, we emphasize the importance of identifying retinal biomarkers in the early stages of AD. However, the examination of AD is not without its challenges, as the similarities shared with other retinal diseases introduce complexity in the search for AD-specific markers. In this review, we address the relevance of using the retina for the early diagnosis of AD and the complex challenges associated with the search for AD-specific retinal biomarkers. We provide a comprehensive overview of the current landscape and highlight avenues for progress in AD diagnosis by retinal examination.

Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.

The association between retina thinning and hippocampal atrophy in Alzheimer's disease and mild cognitive impairment: a meta-analysis and systematic review

Introduction

The retina is the "window" of the central nervous system. Previous studies discovered that retinal thickness degenerates through the pathological process of the Alzheimer's disease (AD) continuum. Hippocampal atrophy is one of the typical clinical features and diagnostic criteria of AD. Former studies have described retinal thinning in normal aging subjects and AD patients, yet the association between retinal thickness and hippocampal atrophy in AD is unclear. The optical coherence tomography (OCT) technique has access the non-invasive to retinal images and magnetic resonance imaging can outline the volume of the hippocampus. Thus, we aim to quantify the correlation between these two parameters to identify whether the retina can be a new biomarker for early AD detection.

Methods

We systematically searched the PubMed, Embase, and Web of Science databases from inception to May 2023 for studies investigating the correlation between retinal thickness and hippocampal volume. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the study quality. Pooled correlation coefficient r values were combined after Fisher's Z transformation. Moderator effects were detected through subgroup analysis and the meta-regression method.

Results

Of the 1,596 citations initially identified, we excluded 1,062 studies after screening the titles and abstract (animal models, n = 99; irrelevant literature, n = 963). Twelve studies met the inclusion criteria, among which three studies were excluded due to unextractable data. Nine studies were eligible for this meta-analysis. A positive moderate correlation between the retinal thickness was discovered in all participants of with AD, mild cognitive impairment (MCI), and normal controls (NC) (r = 0.3469, 95% CI: 0.2490-0.4377, I2 = 5.0%), which was significantly higher than that of the AD group (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%) (p < 0.05). Among different layers, the peripapillary retinal nerve fiber layer (pRNFL) indicated a moderate positive correlation with hippocampal volume (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%). The retinal pigmented epithelium (RPE) was also positively correlated [r = 0.1421, 95% CI:(-0.0447-0.3192), I2 = 84.1%]. The retinal layers and participants were the main overall heterogeneity sources. Correlation in the bilateral hemisphere did not show a significant difference.

Conclusion

The correlation between RNFL thickness and hippocampal volume is more predominant in both NC and AD groups than other layers. Whole retinal thickness is positively correlated to hippocampal volume not only in AD continuum, especially in MCI, but also in NC.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, CRD42022328088.

Retinal imaging biomarkers of Alzheimer's disease: A systematic review and meta-analysis of studies using brain amyloid beta status for case definition

Introduction

We performed a systematic review and meta-analysis of the association between retinal imaging parameters and Alzheimer's disease (AD).

Methods

PubMed, EMBASE, and Scopus were systematically searched for prospective and observational studies. Included studies had AD case definition based on brain amyloid beta (Aβ) status. Study quality assessment was performed. Random-effects meta-analyses of standardized mean difference, correlation, and diagnostic accuracy were conducted.

Results

Thirty-eight studies were included. There was weak evidence of peripapillary retinal nerve fiber layer thinning on optical coherence tomography (OCT) (p = 0.14, 11 studies, n = 828), increased foveal avascular zone area on OCT-angiography (p = 0.18, four studies, n = 207), and reduced arteriole and venule vessel fractal dimension on fundus photography (p < 0.001 and p = 0.08, respectively, three studies, n = 297) among AD cases.

Discussion

Retinal imaging parameters appear to be associated with AD. Small study sizes and heterogeneity in imaging methods and reporting make it difficult to determine utility of these changes as AD biomarkers.

Highlights

We performed a systematic review on retinal imaging and Alzheimer's disease (AD).We only included studies in which cases were based on brain amyloid beta status.Several retinal biomarkers were associated with AD but clinical utility is uncertain.Studies should focus on biomarker-defined AD and use standardized imaging methods.

Through the eyes into the brain, using artificial intelligence

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema

Ocular Biomarkers for Alzheimer Disease Dementia: An Umbrella Review of Systematic Reviews and Meta-analyses

Importance

Several ocular biomarkers have been proposed for the early detection of Alzheimer disease (AD) and mild cognitive impairment (MCI), particularly fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).

Objective

To perform an umbrella review of systematic reviews to assess the diagnostic accuracy of ocular biomarkers for early diagnosis of Alzheimer disease.

Data Sources

MEDLINE, Embase, and PsycINFO were searched from January 2000 to November 2021. The references of included reviews were also searched.

Study Selection

Systematic reviews investigating the diagnostic accuracy of ocular biomarkers to detect AD and MCI, in secondary care or memory clinics, against established clinical criteria or clinical judgment.

Data Extraction and Synthesis

The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline checklist was followed and the Risk Of Bias in Systematic reviews tool was used to assess review quality.

Main Outcomes and Measures

The prespecified outcome was the accuracy of ocular biomarkers for diagnosing AD and MCI. The area under the curve (AUC) was derived from standardized mean difference.

Results

From the 591 titles, 14 systematic reviews were included (median [range] number of studies in each review, 14 [5-126]). Only 4 reviews were at low risk of bias on all Risk of Bias in Systematic Reviews domains. The imaging-derived parameters with the most evidence for detecting AD compared with healthy controls were OCT peripapillary retinal nerve fiber layer thickness (38 studies including 1883 patients with AD and 2510 controls; AUC = 0.70; 95% CI, 0.53-0.79); OCTA foveal avascular zone (5 studies including 177 patients with AD and 371 controls; AUC = 0.73; 95% CI, 0.50-0.89); and saccadic eye movements prosaccade latency (30 studies including 651 patients with AD/MCI and 771 controls; AUC = 0.64; 95% CI, 0.58-0.69). Antisaccade error was investigated in fewer studies (12 studies including 424 patients with AD/MCI and 382 controls) and yielded the best accuracy (AUC = 0.79; 95% CI, 0.70-0.88).

Conclusions and Relevance

This umbrella review has highlighted limitations in design and reporting of the existing research on ocular biomarkers for diagnosing AD. Parameters with the best evidence showed poor to moderate diagnostic accuracy in cross-sectional studies. Future longitudinal studies should investigate whether changes in OCT and OCTA measurements over time can yield accurate predictions of AD onset.

Research Trends and Hotspots of Retinal Optical Coherence Tomography: A 31-Year Bibliometric Analysis

The emergence of optical coherence tomography (OCT) over the past three decades has sparked great interest in retinal research. However, a comprehensive analysis of the trends and hotspots in retinal OCT research is currently lacking. We searched the publications on retinal OCT in the Web of Science database from 1991 to 2021 and performed the co-occurrence keyword analysis and co-cited reference network using bibliometric tools. A total of 25,175 publications were included. There has been a progressive increase in the number of publications. The keyword co-occurrence network revealed five clusters of hotspots: (1) thickness measurements; (2) therapies for macular degeneration and macular edema; (3) degenerative retinal diseases; (4) OCT angiography (OCTA); and (5) vitrectomy for macular hole and epiretinal membrane. The co-citation analysis displayed 26 highly credible clusters (S = 0.9387) with a well-structured network (Q = 0.879). The major trends of research were: (1) thickness measurements; (2) therapies for macular degeneration and macular edema; and (3) OCTA. Recent emerging frontiers showed a growing interest in OCTA, vessel density, choriocapillaris, central serous chorioretinopathy, Alzheimer’s disease, and deep learning. This review summarized 31 years of retinal OCT research, shedding light on the hotspots, main themes, and emerging frontiers to assist in future research.

Optical coherence tomography angiography biomarkers of microvascular alterations in RVCL-S

Background

The brain and retina share many neuronal and vasculature characteristics. We investigated the retinal microvasculature in patients with a monogenic vasculopathy using optical coherence tomography angiography (OCTA). OCT-A is a novel precise non-invasive imaging method that may provide biomarkers suitable for diagnosis and follow-up of small vessel diseases.

Methods

In this exploratory cross-sectional study, eleven RVCL-S patients and eleven age-matched healthy control participants were included. The size of the foveal avascular zone (FAZ) and the vascular density of the superficial capillary networks in the retina were measured by OCT-A.

Results

The symptomatic and presymptomatic patients showed significantly lower vascular density values than controls in the foveal region [median (IQR) 18.2% (15.8–18.6) vs. 24.4% (21.5–26.8) (p < 0.001), 29.8% (29.6–30.8) vs. 33.2% (32.0–33.6) (p = 0.002), respectively]. The FAZ was significantly larger in the symptomatic RVCL-S patients than in the control group [13,416 square pixels [7,529–22,860] vs. 1,405 square pixels [1,344–2,470] (p < 0.001)]. No significant difference was identified in measurements of FAZ comparing presymptomatic and controls.

Conclusion

Our findings with OCT-A demonstrated that RVCL-S causes an increase in the size of the FAZ in symptomatic RVCL-S patients compared to healthy participants. Moreover, there is a decrease in vessel density in the superficial capillary networks in both symptomatic and presymptomatic patients. In the future, newly developed precise objective instruments such as OCT (-A) may provide important tools in determining disease activity for follow up of common small vessel diseases.

Retinal Vascular Study Using OCTA in Subjects at High Genetic Risk of Developing Alzheimer’s Disease and Cardiovascular Risk Factors

In 103 subjects with a high genetic risk of developing Alzheimer’s disease (AD), family history (FH) of AD and ApoE ɛ4 characterization (ApoE ɛ4)) were analyzed for changes in the retinal vascular network by OCTA (optical coherence tomography angiography), and AngioTool and Erlangen-Angio-Tool (EA-Tool) as imaging analysis software. Retinal vascularization was analyzed by measuring hypercholesterolemia (HCL) and high blood pressure (HBP). Angio-Tool showed a statistically significant higher percentage of area occupied by vessels in the FH+ ApoE ɛ4- group vs. in the FH+ ApoE ɛ4+ group, and EA-Tool showed statistically significant higher vascular densities in the C3 ring in the FH+ ApoE ɛ4+ group when compared with: i)FH- ApoE ɛ4- in sectors H3, H4, H10 and H11; and ii) FH+ ApoE ɛ4- in sectors H4 and H12. In participants with HCL and HBP, statistically significant changes were found, in particular using EA-Tool, both in the macular area, mainly in the deep plexus, and in the peripapillary area. In conclusion, OCTA in subjects with genetic risk factors for the development of AD showed an apparent increase in vascular density in some sectors of the retina, which was one of the first vascular changes detectable. These changes constitute a promising biomarker for monitoring the progression of pathological neuronal degeneration.