Artificial intelligence in OCT angiography

Artificial intelligence in therapeutic management of hyperlipidemic ocular pathology

Hyperlipidemia has many ocular manifestations, the most prevalent being retinal vascular occlusion. Hyperlipidemic lesions and occlusions to the vessels supplying the retina result in permanent blindness, necessitating prompt detection and treatment. Retinal vascular occlusion is diagnosed using different imaging modalities, including optical coherence tomography angiography. These diagnostic techniques obtain images representing the blood flow through the retinal vessels, providing an opportunity for AI to utilize image recognition to detect blockages and abnormalities before patients present with symptoms. AI is already being used as a non-invasive method to detect retinal vascular occlusions and other vascular pathology, as well as predict treatment outcomes. As providers see an increase in patients presenting with new retinal vascular occlusions, the use of AI to detect and treat these conditions has the potential to improve patient outcomes and reduce the financial burden on the healthcare system. This article comprehends the implications of AI in the current management strategies of retinal vascular occlusion (RVO) in hyperlipidemia and the recent developments of AI technology in the management of ocular diseases.

Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review

PURPOSE

To evaluate the evidence for alterations of blood flow, vascular and perfusion densities in the choroid, macula, peripapillary region, and the area surrounding the optic nerve head (ONH) in patients with thyroid-associated ophthalmopathy (TAO) based on changes of OCTA parameters.

METHODS

A systematic review of Pubmed, Google Scholar, Scopus, WOS, Cochrane, and Embase databases, including quality assessment of published studies, investigating the alterations of OCTA parameters in TAO patients was conducted. The outcomes of interest comprised changes of perfusion and vascular densities in radial peripapillary capillary (RPC), ONH, superficial and deep retinal layers (SRL and DRL), choriocapillaris (CC) flow, and the extent of the foveal avascular zone (FAZ).

RESULTS

From the total of 1253 articles obtained from the databases, the pool of papers was narrowed down to studies published until March 20th, 2024. Lastly, 42 studies were taken into consideration which contained the data regarding the alterations of OCTA parameters including choriocapillary vascular flow, vascular and perfusion densities of retinal microvasculature, SRL, and DRL, changes in macular all grid sessions, changes of foveal, perifoveal and parafoveal densities, macular whole image vessel density (m-wiVD) and FAZ, in addition to alterations of ONH and RPC whole image vessel densities (onh-wiVD and rpc-wiVD) among TAO patients. The correlation of these parameters with visual field-associated parameters, such as Best-corrected visual acuity (BCVA), Visual field mean defect (VF-MD), axial length (AL), P100 amplitude, and latency, was also evaluated among TAO patients.

CONCLUSION

The application of OCTA has proven helpful in distinguishing active and inactive TAO patients, as well as differentiation of patients with or without DON, indicating the potential promising role of some OCTA measures for early detection of TAO with high sensitivity and specificity in addition to preventing the irreversible outcomes of TAO. OCTA assessments have also been applied to evaluate the effectiveness of TAO treatment approaches, including systemic corticosteroid therapy and surgical decompression.

Choroidal vascularity index in patients with computer vision syndrome combined with accommodative lead

BACKGROUND

This study aimed to investigate the choroidal vascularity index (CVI) in patients with computer vision syndrome (CVS) combined with accommodative lead.

METHODS

This retrospective case-control study enrolled patients diagnosed with CVS and accommodative lead at University-Town Hospital of Chongqing Medical University between July 2022 and May 2023. The control group included individuals without any ocular diseases. Ophthalmic assessments included basic visual acuity, refraction, ocular biometric parameters, and CVI.

RESULTS

A total of 85 participants were included in the study, with 45 in the CVS group and 40 in the control group. The central corneal thickness of CVS group was found to be significantly thinner compared to the control group in both the right eye (532.40±30.93 vs. 545.78±19.99 µm, P = 0.019) and left eye (533.96±29.57 vs. 547.56±20.39, P = 0.014). In comparison to the control group, the CVS group exhibited lower CVI in the superior (0.40±0.08 vs. 0.43±0.09, P = 0.001), temporal (0.40±0.08 vs. 0.44±0.10, P < 0.001), inferior (0.41±0.08 vs. 0.46±0.08, P < 0.001), and nasal (0.41±0.08 vs. 0.44±0.08, P = 0.001) quadrants. Similar differences were observed in all four quadrants within the 1-3 mm radius, and in the temporal (P = 0.004) and inferior (P = 0.002) quadrants within the 1-6 mm and 3-6 mm radii (all P < 0.05).

CONCLUSION

Compared to individuals without ocular issues, patients with CVS and accommodative lead were found to have thinner corneal central thickness and lower CVI.

Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE)

Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network

Purpose

To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA).

Methods

This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset.

Results

The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%).

Conclusions

A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy.

Translational Relevance

Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

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.

Reduced Retinal Blood Vessel Densities Measured by Optical Coherence Tomography Angiography in Keratoconus Patients Are Negatively Correlated with Keratoconus Severity

Keratoconus (KC) is the most common corneal ectasia. Optical coherence tomography angiography (OCT-A) is a relatively new non-invasive imaging technique that allows the visualization and quantification of retinal and choriocapillary blood vessels. The aim of this study is to assess retinal and choriocapillary vessel density (VD) differences between KC patients and healthy controls and to investigate correlations between VD and KC severity. Fifty-two eyes were included in this exploratory study: twenty-six eyes from 26 KC patients and twenty-six eyes from 26 age- and gender-matched healthy controls. All patients underwent Scheimpflug corneal topography with Pentacam, axis lengths measurement and optical coherence tomography angiography (OCT-A). The thinnest spot in corneal pachymetry, maximum K (Kmax) and KC severity indices from the Belin/Ambrósio enhanced ectasia display (BAD) were also assessed. There was a distinct reduction particularly in the retinal VD of the superficial capillary plexus (SCP). Correlation analyses showed strong and moderate negative correlations between the VD in the macular SCP and BAD KC scores and between the SCP VD and Kmax. There was no difference in retinal thickness between the KC and healthy controls. With this study, further evidence for altered VD measurements by OCT-A in KC patients is given. For the first time, we demonstrated negative correlations between BAD KC scores and retinal blood vessel alterations. A major limitation of the study is the relatively small sample size. Since an artefactual reduction of the quantitative OCT-A measurements due to irregular corneal topography in KC must be assumed, it remains to be investigated whether there are also actual changes in the retinal microcirculation in KC.

Distribution and determinants of choroidal vascularity index in healthy eyes from deep-learning choroidal analysis: a population-based SS-OCT study

AIMS

To quantify the profiles of choroidal vascularity index (CVI) using fully artificial intelligence (AI)-based algorithm applied to swept-source optical coherence tomography (SS-OCT) images and evaluate the determinants of CVI in a population-based study.

METHODS

This cross-sectional study included adults aged ≥35 years residing in the Yuexiu District of Guangzhou, China, a follow-up population-based study. All participants (n=646) underwent comprehensive ophthalmic examinations, including SS-OCT for quantifying choroidal parameters. The CVI and subfoveal choroidal thickness (SFCT) were measured by a novel AI-based system.

RESULTS

A total of 556 participants were included, with a mean age of 56.4±9.9 years and 44.96% women. The average CVI and SFCT of the overall population were 69.7% (95% CI 69.2 to 70.3) and 263.0 µm (95% CI 257.2 to 268.8), respectively. After adjusting for other factors, older age and longer AL were significantly associated with a lower CVI. The CVI decreased by -0.13% (-0.19 to -0.06, p<0.001) with each 1-year increase in age, -2.10% (-3.29 to -0.92, p=0.001) with each 1 mm increase in AL. Furthermore, significantly positive correlation between CVI and SFCT has been observed, with coefficient of 0.059 (0.052 to 0.065, p<0.001).

CONCLUSION

Using new AI-based choroidal segmentation software, we provided a fast, reliable and objective CVI profile for large-scale samples. Older age and longer AL were independent correlates of choroidal thinning and CVI decline. These factors should be considered when interpreting SS-OCT-based choroidal measurements.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION

PURPOSE

The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans.

METHODS

Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories.

RESULTS

The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021).

CONCLUSION

These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Optical Coherence Tomography Angiography: A 2023 Focused Update on Age-Related Macular Degeneration

Optical coherence tomography angiography (OCTA) has extensively enhanced our comprehension of eye microcirculation and of its associated diseases. In this narrative review, we explored the key concepts behind OCTA, as well as the most recent evidence in the pathophysiology of age-related macular degeneration (AMD) made possible by OCTA. These recommendations were updated since the publication in 2020, and are targeted for 2023. Importantly, as a future perspective in OCTA technology, we will discuss how artificial intelligence has been applied to OCTA, with a particular emphasis on its application to AMD study.

Accelerating Patterned Vascularization Using Granular Hydrogel Scaffolds and Surgical Micropuncture

Bulk hydrogel scaffolds are common in reconstructive surgery. They allow for the staged repair of soft tissue loss by providing a base for revascularization. Unfortunately, they are limited by both slow and random vascularization, which may manifest as treatment failure or suboptimal repair. Rapidly inducing patterned vascularization within biomaterials has profound translational implications for current clinical treatment paradigms and the scaleup of regenerative engineering platforms. To address this long-standing challenge, a novel microsurgical approach and granular hydrogel scaffold (GHS) technology are co-developed to hasten and pattern microvascular network formation. In surgical micropuncture (MP), targeted recipient blood vessels are perforated using a microneedle to accelerate cell extravasation and angiogenic outgrowth. By combining MP with an adjacent GHS with precisely tailored void space architecture, microvascular pattern formation as assessed by density, diameter, length, and intercapillary distance is rapidly guided. This work opens new translational opportunities for microvascular engineering, advancing reconstructive surgery, and regenerative medicine.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map

OBJECTIVE

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers' decision-making.

METHODS

A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans.

RESULTS

The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid.

CONCLUSION/SIGNIFICANCE

A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Detection of systemic cardiovascular illnesses and cardiometabolic risk factors with machine learning and optical coherence tomography angiography: a pilot study

BACKGROUND/OBJECTIVES

Optical coherence tomography angiography (OCTA) has been found to identify changes in the retinal microvasculature of people with various cardiometabolic factors. Machine learning has previously been applied within ophthalmic imaging but has not yet been applied to these risk factors. The study aims to assess the feasibility of predicting the presence or absence of cardiovascular conditions and their associated risk factors using machine learning and OCTA.

METHODS

Cross-sectional study. Demographic and co-morbidity data was collected for each participant undergoing 3 × 3 mm, 6 × 6 mm and 8 × 8 mm OCTA scanning using the Carl Zeiss CIRRUS HD-OCT model 5000. The data was then pre-processed and randomly split into training and testing datasets (75%/25% split) before being applied to two models (Convolutional Neural Network and MoblieNetV2). Once developed on the training dataset, their performance was assessed on the unseen test dataset.

RESULTS

Two hundred forty-seven participants were included. Both models performed best in predicting the presence of hyperlipidaemia in 3 × 3 mm scans with an AUC of 0.74 and 0.81, and accuracy of 0.79 for CNN and MobileNetV2 respectively. Modest performance was achieved in the identification of diabetes mellitus, hypertension and congestive heart failure in 3 × 3 mm scans (all with AUC and accuracy >0.5). There was no significant recognition for 6 × 6 and 8 × 8 mm for any cardiometabolic risk factor.

CONCLUSION

This study demonstrates the strength of ML to identify the presence cardiometabolic factors, in particular hyperlipidaemia, in high-resolution 3 × 3 mm OCTA scans. Early detection of risk factors prior to a clinically significant event, will assist in preventing adverse outcomes for people.

Optical coherence tomography angiography in diabetic retinopathy

There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.

Single-shot OCT and OCT angiography for slab-specific detection of diabetic retinopathy

In this study, we present an optical coherence tomographic angiography (OCTA) prototype using a 500 kHz high-speed swept-source laser. This system can generate a 75-degree field of view with a 10.4 µm lateral resolution with a single acquisition. With this prototype we acquired detailed, wide-field, and plexus-specific images throughout the retina and choroid in eyes with diabetic retinopathy, detecting early retinal neovascularization and locating pathology within specific retinal slabs. Our device could also visualize choroidal flow and identify signs of key biomarkers in diabetic retinopathy.

Detection and Classification of Diabetic Macular Edema with a Desktop-Based Code-Free Machine Learning Tool

Objectives

To evaluate the effectiveness of the Lobe application, a machine learning (ML) tool that can be used on a personal computer without requiring coding expertise, in the recognition and classification of diabetic macular edema (DME) in spectral-domain optical coherence tomography (SD-OCT) scans.

Materials and Methods

A total of 695 cross-sectional SD-OCT images from 336 patients with DME and 200 OCT images of 200 healthy controls were included. Images with DME were classified into three main types: diffuse retinal edema (DRE), cystoid macular edema (CME), and cystoid macular degeneration (CMD). To develop the ML model, we used the desktop-based code-free Lobe application, which includes a pre-trained ResNet-50 V2 convolutional neural network and is available free of charge. The performance of the trained model in recognizing and classifying DME was evaluated with 41 DRE, 28 CMD, 70 CME, and 40 normal SD-OCT images that were not used in the training.

Results

The developed model showed 99.28% sensitivity and 100% specificity for class-independent detection of DME. Sensitivity and specificity by labels were 87.80% and 98.57% for DRE, 96.43% and 99.29% for CME, and 95.71% and 95.41% for CMD, respectively.

Conclusion

To our knowledge, this is the first evaluation of the effectiveness of Lobe with ophthalmological images, and the results indicate that it can be used with high efficiency in the recognition and classification of DME from SD-OCT images by ophthalmologists without coding expertise.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.

Optical coherence tomography angiography: a review of the current literature

This narrative review presents a comprehensive examination of optical coherence tomography angiography (OCTA), a non-invasive retinal vascular imaging technology, as reported in the existing literature. Building on the coherence tomography principles of standard OCT, OCTA further delineates the retinal vascular system, thus offering an advanced alternative to conventional dye-based imaging. OCTA provides high-resolution visualisation of both the superficial and deep capillary networks, an achievement previously unattainable. However, image quality may be compromised by factors such as motion artefacts or media opacities, potentially limiting the utility of OCTA in certain patient cohorts. Despite these limitations, OCTA has various potential clinical applications in managing retinal and choroidal vascular diseases. Still, given its considerable cost implications relative to current modalities, further research is warranted to justify its broader application in clinical practice.

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Optical Coherence Tomography Angiography in Retinal Vascular Disorders

Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods-in particular, deep learning-show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.

Acoustic radiation force optical coherence elastography: A preliminary study on biomechanical properties of trabecular meshwork

Evaluating biomechanical properties of trabecular meshwork (TM) is of great significance for understanding the mechanism of aqueous humor circulation and its relationship to some eye diseases such as glaucoma; however, there is almost no relevant study due to the lack of clinical measurement tool. In this paper, an acoustic radiation force optical coherence elastography (ARF-OCE) system is developed with the advantages of noninvasive detection, high resolution, high sensitivity, and high-speed imaging, by which elastic modulus of the porcine and human TMs is accurately quantified. As the first OCE imaging of TM, our study demonstrates that ARF-OCE may be an effective approach to advance the research of diseases related to aqueous humor circulation.

Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases

Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.

A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.

OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy

Similar to ultrasound adapting soundwaves to depict the inner structures and tissues, optical coherence tomography (OCT) utilizes low coherence light waves to assess characteristics in the eye. Compared to the previous gold standard diagnostic imaging fluorescein angiography, OCT is a noninvasive imaging modality that generates images of ocular tissues at a rapid speed. Two commonly used iterations of OCT include spectral-domain (SD) and swept-source (SS). Each comes with different wavelengths and tissue penetration capacities. OCT angiography (OCTA) is a functional extension of the OCT. It generates a large number of pixels to capture the tissue and underlying blood flow. This allows OCTA to measure ischemia and demarcation of the vasculature in a wide range of conditions. This review focused on the study of four commonly encountered diseases involving the retina including age-related macular degeneration (AMD), diabetic retinopathy (DR), central serous chorioretinopathy (CSC), and macular telangiectasia (MacTel). Modern imaging techniques including SD-OCT, TD-OCT, SS-OCT, and OCTA assist with understanding the disease pathogenesis and natural history of disease progression, in addition to routine diagnosis and management in the clinical setting. Finally, this review compares each imaging technique's limitations and potential refinements.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

Characterization of the Retinal Microvasculature and FAZ Changes in Ischemic Stroke and Its Different Types

Purpose

This study aimed to assess morphological changes in the retinal microvasculature and foveal avascular zone (FAZ) in patients with ischemic stroke and its different subtypes.

Methods

Thirty-three patients with ischemic stroke (14 with nonlacunar infarction and 19 with lacunar infarction) and 27 control participants were enrolled in this study. Based on optical coherence tomography angiography (OCTA), three vascular parameters, including vascular area density, vascular fractal dimension (VFD), and vascular orientation distribution (VOD), and four FAZ-related parameters, including FAZ area, FAZ axis ratio (FAR), FAZ circularity (FC), and FAZ roundness, in the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were extracted and analyzed.

Results

Logistic regression results showed that worse best-corrected visual acuity (odds ratio [OR], 0.21), higher FAR (OR, 2.77) and lower FC (OR, 0.36) of the DCP were associated with ischemic stroke. Furthermore, lower VOD of the SCP was significantly associated with lacunar infarction compared with nonlacunar infarction.

Conclusions

Our study shows that the FAR and FC of the DCP may be potential biomarkers of ischemic stroke. Moreover, we demonstrated that OCT showed specific damage patterns in retinal microvascular and macular morphology in different subtypes of ischemic stroke.

Translational Relevance

This work lays the foundation for the pathophysiological characteristics of cerebrovascular diseases assisted by retinal imaging and artificial intelligence.

Studies of the retinal microcirculation using human donor eyes and high-resolution clinical imaging: Insights gained to guide future research in diabetic retinopathy

The microcirculation plays a key role in delivering oxygen to and removing metabolic wastes from energy-intensive retinal neurons. Microvascular changes are a hallmark feature of diabetic retinopathy (DR), a major cause of irreversible vision loss globally. Early investigators have performed landmark studies characterising the pathologic manifestations of DR. Previous works have collectively informed us of the clinical stages of DR and the retinal manifestations associated with devastating vision loss. Since these reports, major advancements in histologic techniques coupled with three-dimensional image processing has facilitated a deeper understanding of the structural characteristics in the healthy and diseased retinal circulation. Furthermore, breakthroughs in high-resolution retinal imaging have facilitated clinical translation of histologic knowledge to detect and monitor progression of microcirculatory disturbances with greater precision. Isolated perfusion techniques have been applied to human donor eyes to further our understanding of the cytoarchitectural characteristics of the normal human retinal circulation as well as provide novel insights into the pathophysiology of DR. Histology has been used to validate emerging in vivo retinal imaging techniques such as optical coherence tomography angiography. This report provides an overview of our research on the human retinal microcirculation in the context of the current ophthalmic literature. We commence by proposing a standardised histologic lexicon for characterising the human retinal microcirculation and subsequently discuss the pathophysiologic mechanisms underlying key manifestations of DR, with a focus on microaneurysms and retinal ischaemia. The advantages and limitations of current retinal imaging modalities as determined using histologic validation are also presented. We conclude with an overview of the implications of our research and provide a perspective on future directions in DR research.

Advanced ultrawide-field optical coherence tomography angiography identifies previously undetectable changes in biomechanics-related parameters in nonpathological myopic fundus

Purpose: To detect previously undetectable changes in vessel density and structural thickness, the two biomechanics-related parameters reflecting hemodynamics and tensile strength, respectively, in the peripheral and central fundi of nonpathological myopic eyes with an advanced ultrawide-field optical coherence tomography angiography (OCTA) system.

Methods: A cross-sectional observational clinical study was carried out by recruiting 155 eyes from 79 college students aged 18–28 years. The eyes were stratified into normal, low-myopia, medium-myopia, and high-myopia groups according to diopter. A newly developed OCTA system with scanning dimensions of 24 mm × 20 mm, acquisition speed of 400 kHz, and imaging range of 6 mm was used to examine the vessel densities of superficial vascular complex (SVC), deep vascular complex (DVC), choriocapillary (ChC), and choroidal vessel (ChV) layers, as well as the thicknesses of the inner retina, outer retina, and choroid in the nonpathological myopic eyes.

Results: The vessel densities in ChV at the temporal, inferotemporal, inferior, and inferonasal regions in the fundus periphery were significantly reduced in myopic subjects as compared to normal controls (all p < 0.05). The thicknesses of the inner retinal segments in most peripheral regions of the fundus became attenuated along with myopia severity (all p < 0.05). The thicknesses of the outer retinal segments were diminished at the superior and supranasal regions of the peripheral fundi of myopic subjects as compared to normal controls (all p < 0.05). At the central macular region, the decreased vessel densities of SVC and DVC were correlated with the attenuated thicknesses of inner retinal segments, respectively (all p < 0.05).

Conclusion: As revealed for the first time by the advanced ultrawide-field OCTA system, the two biomechanics-related parameters that include the densities of the choroidal vessels and thicknesses of the inner retina segments were significantly reduced in the periphery of nonpathological myopic fundi and the reductions were associated with myopia severity. At the central macular region, the newly developed device provides consistent results with the previous findings. Therefore, it is important to use the noninvasive, ultrawide-field OCTA with high resolution for early detection of fundus changes in subjects with nonpathological high myopia.

Clinical Trial Registration: clinicaltrials.gov, identifier ChiCTR2100054093.

Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images

The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm³ and 104.0 ± 18.9 mm² in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Retinal Microvascular Signs in Pre- and Early-Stage Diabetic Retinopathy Detected Using Wide-Field Swept-Source Optical Coherence Tomographic Angiography

Purpose Using a wide-field, high-resolution swept-source optical coherence tomographic angiography (OCTA), this study investigated microvascular abnormalities in patients with pre- and early-stage diabetic retinopathy. Methods 38 eyes of 20 people with diabetes mellitus (DM) type 2 without diabetic retinopathy (DR) and 39 eyes of 21 people with DR were enrolled in this observational and cross-sectional cohort study, and a refractive error-matched group consisting of 42 eyes of 21 non-diabetic subjects of similar age were set as the control. Each participant underwent a wide-field swept-source OCTA. On OCTA scans (1.2 cm × 1.2 cm), the mean central macular thickness (CMT), the vessel density of the inner retina, superficial capillary plexus (SCP), and deep capillary plexus (DCP) were independently measured in the whole area (1.2 cm diameter) via concentric rings with varying radii (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 cm). Results Patients whose eyes had pre-and early-stage DR showed significantly decreased vessel density in the inner retina, SCP, DCP and CMT (early-stage DR) compared with the control. In addition, compared with the average values upon wide-field OCTA, the decreases were even more pronounced for concentric rings with a radius of 0.9–1.2 cm in terms of the inner retina, SCP, DCP and CMT. Conclusions Widefield OCTA allows for a more thorough assessment of retinal changes in patients with pre- and early-stage DR.; retinal microvascular abnormalities were observed in both groups. In addition, the decreases in retinal vessel density were more significant in the peripheral concentric ring with a radius of 0.9–1.2 cm. The application of novel and wide-field OCTA could potentially help to detect earlier diabetic microvascular abnormalities.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography

Purpose

Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Methods

Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.

Results

For rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.

Conclusions

A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.

Translational Relevance

The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

Self-Supervised Sequence Recovery for Semi-Supervised Retinal Layer Segmentation

Automated layer segmentation plays an important role for retinal disease diagnosis in optical coherence tomography (OCT) images. However, the severe retinal diseases result in the performance degeneration of automated layer segmentation approaches. In this paper, we present a robust semi-supervised retinal layer segmentation network to relieve the model failures on abnormal retinas, in which we obtain the lesion features from the labeled images with disease-balanced distribution, and utilize the unlabeled images to supplement the layer structure information. Specifically, in our proposed method, the cross-consistency training is utilized over the predictions of the different decoders, and we enforce a consistency between different decoder predictions to improve the encoders representation. Then, we proposed a sequence prediction branch based on self-supervised manner, which is designed to predict the position of each jigsaw puzzle to obtain sensory perception of the retinal layer structure. To this task, a layer spatial pyramid pooling (LSPP) module is designed to extract multi-scale layer spatial features. Furthermore, we use the optical coherence tomography angiography (OCTA) to supplement the information damaged by diseases. The experimental results validate that our method achieves more robust results compared with current supervised segmentation methods. Meanwhile, advanced segmentation performance can be obtained compared with state-of-the-art semi-supervised segmentation methods.

Towards standardizing retinal optical coherence tomography angiography: a review

The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.

Assessment of choroidal structural changes in patients with pre- and early-stage clinical diabetic retinopathy using wide-field SS-OCTA

PURPOSE

To investigate the micro-vascular changes in choroidal structures in patients with pre- and early-stage clinical diabetic retinopathy (DR) using wide-field Swept-Source Optical Coherence Tomography Angiography (SS-OCTA).

METHOD

This observational cross-sectional study included 131 eyes of 68 subjects that were divided into healthy controls (group 1, n = 46), pre-DR (group 2, n = 43), early-stage DR (group 3, n = 42) cohorts. All participants that underwent SS-OCTA examination were inpatients in the department of Ophthalmology and the department of Endocrinology, Qilu Hospital, Shandong University, and Department of Ophthalmology, Aier Eye Hospital, Jinan, from July 11, 2021 to March 17, 2022. The choroidal vascularity index (CVI), choroidal thickness (ChT) and central macular thickness (CMT) in the whole area (diameter of 12 mm) and concentric rings with different ranges (0-3, 3-6, 6-9, and 9-12 mm) were recorded and analyzed from the OCTA image.

RESULT

Compared with healthy eyes, decreases in CVI and ChT were found in the eyes of patients with pre-or early-stage DR. The changes were more significant in the peripheral choroid, with the most prominent abnormalities in the 9-12mm area (P < 0.001). However, there was no obvious difference in the average CMT value. Furthermore, CVI and ChT were significantly correlated with the duration of diabetes in the range of 6-9 and 9-12 mm (Ps < 0.05; Correlation coefficient = -0.549, -0.395, respectively), with the strongest correlation (Ps < 0.01; Correlation coefficient = -0.597, -0.413, respectively) observed at 9-12 mm.

CONCLUSION

The CVI and ChT values of diabetic patients are significantly lower than in healthy controls, especially in patients with early-stage DR. In addition, the peripheral choroidal capillaries are more susceptible to early DM-induced injury than in the central area.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Purpose

We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).

Methods

In this study, 6-mm × 6-mm macular scans with a 400 × 400 A-line sampling density and 3-mm × 3-mm scans with a 304 × 304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc., Fremont, California, USA). Projection-resolved OCTA algorithm removed projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the respective plexus. We proposed a deep learning-based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. We applied the same network on 3-mm × 3-mm angiograms to enhance these images further. We evaluated the reconstructed 3-mm × 3-mm and 6-mm × 6-mm angiograms based on vascular connectivity, Weber contrast, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone.

Results

Compared to the originals, the Deep Capillary Angiogram Reconstruction Network (DCARnet)-enhanced 6-mm × 6-mm angiograms had significantly reduced noise intensity (ICP, 7.38 ± 25.22, P < 0.001; DCP, 11.20 ± 22.52, P < 0.001), improved vascular connectivity (ICP, 0.95 ± 0.01, P < 0.001; DCP, 0.96 ± 0.01, P < 0.001), and enhanced Weber contrast (ICP, 4.25 ± 0.10, P < 0.001; DCP, 3.84 ± 0.84, P < 0.001), without generating false flow signal when noise intensity lower than 650. The DCARnet-enhanced 3-mm × 3-mm angiograms also reduced noise, improved connectivity, and enhanced Weber contrast in 3-mm × 3-mm ICP and DCP angiograms from 101 eyes. In addition, DCARnet preserved the appearance of the dilated vessels in the reconstructed angiograms in diabetic eyes.

Conclusions

DCARnet can enhance 3-mm × 3-mm and 6-mm × 6-mm ICP and DCP angiogram image quality without introducing artifacts.

Translational Relevance

The enhanced 6-mm × 6-mm angiograms may be easier for clinicians to interpret qualitatively.

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

Detecting retinal cell stress and apoptosis with DARC: Progression from lab to clinic

DARC (Detection of Apoptosing Retinal Cells) is a retinal imaging technology that has been developed within the last 2 decades from basic laboratory science to Phase 2 clinical trials. It uses ANX776 (fluorescently labelled Annexin A5) to identify stressed and apoptotic cells in the living eye. During its development, DARC has undergone biochemistry optimisation, scale-up and GMP manufacture and extensive preclinical evaluation. Initially tested in preclinical glaucoma and optic neuropathy models, it has also been investigated in Alzheimer, Parkinson's and Diabetic models, and used to assess efficacy of therapies. Progression to clinical trials has not been speedy. Intravenous ANX776 has to date been found to be safe and well-tolerated in 129 patients, including 16 from Phase 1 and 113 from Phase 2. Results on glaucoma and AMD patients have been recently published, and suggest DARC with an AI-aided algorithm can be used to predict disease activity. New analyses of DARC in GA prediction are reported here. Although further studies are needed to validate these findings, it appears there is potential of the technology to be used as a biomarker. Much larger clinical studies will be needed before it can be considered as a diagnostic, although the relatively non-invasive nature of the nasal as opposed to intravenous administration would widen its acceptability in the future as a screening tool. This review describes DARC development and its progression into Phase 2 clinical trials from lab-based research. It discusses hypotheses, potential challenges, and regulatory hurdles in translating technology.

Quantification of Nonperfusion Area in Montaged Widefield OCT Angiography Using Deep Learning in Diabetic Retinopathy

Purpose

To examine the efficacy of a deep learning-based algorithm to quantify the nonperfusion area (NPA) on montaged widefield OCT angiography (OCTA) for assessment of diabetic retinopathy (DR) severity.

Design

Cross-sectional study.

Participants

One hundred thirty-seven participants with a full range of DR severity and 26 healthy participants.

Methods

A deep learning-based algorithm was developed for detecting and quantifying NPA in the superficial vascular complex on widefield OCTA comprising 3 horizontally montaged 6 × 6-mm OCTA scans from the nasal, macular, and temporal regions. We trained the algorithm on 978 volumetric OCTA scans from all participants using 5-fold cross-validation. The algorithm can distinguish NPA from shadow artifacts. The F1 score evaluated segmentation accuracy. The area under the receiver operating characteristic curve and sensitivity with specificity fixed at 95% quantified network performance to distinguish patients with diabetes from healthy control participants, referable DR from nonreferable DR (nonproliferative DR [NPDR] less than moderate severity), and severe DR (severe NPDR, proliferative DR, or DR with edema) from nonsevere DR (mild to moderate NPDR).

Main Outcome Measures

Widefield OCTA NPA, visual acuity (VA), and DR severities.

Results

Automatically segmented NPA showed high agreement with the manually delineated ground truth, with a mean ± standard deviation F1 score of 0.78 ± 0.05 in nasal, 0.82 ± 0.07 in macular, and 0.78 ± 0.05 in temporal scans. The extrafoveal avascular area (EAA) in the macular scan showed the best sensitivity at 54% for differentiating those with diabetes from healthy control participants, whereas montaged widefield OCTA scan showed significantly higher sensitivity than macular scans (P < 0.0001, McNemar’s test) for detecting eyes with DR at 66%, referable DR at 63%, and severe DR at 62%. Montaged widefield OCTA showed the highest correlation (Spearman ρ = 0.74; P < 0.0001) between EAA and DR severity. The macular scan showed the strongest negative correlation (Pearson ρ = –0.42; P < 0.0001) between EAA and best-corrected VA.

Conclusions

A deep learning-based algorithm for montaged widefield OCTA can detect NPA accurately and can improve the detection of clinically important DR.