An end-to-end network for segmenting the vasculature of three retinal capillary plexuses from OCT angiographic volumes

Safety and effectivity of Kono-S anastomosis in Crohn's patients: a systematic review and Meta-analysis

PURPOSE

Kono-S anastomosis, an antimesenteric, functional, end-to-end handsewn anastomosis, was introduced in 2011. The aim of this meta-analysis is to evaluate the safety and effectivity of the Kono-S technique.

METHODS

A comprehensive search of MEDLINE (PubMed), Embase (Elsevier), Scopus (Elsevier), and Cochrane Central (Ovid) from inception to August 24th, 2023, was conducted. Studies reporting outcomes of adults with Crohn's disease undergoing ileocolic resection with subsequent Kono-S anastomosis were included. PRISMA and Cochrane guidelines were used to screen, extract and synthesize data. Primary outcomes assessed were endoscopic, surgical and clinical recurrence rates, as well as complication rates. Data were pooled using random-effects models, and heterogeneity was assessed with I² statistics. ROBINS-I and ROB2 tools were used for quality assessment.

RESULTS

12 studies involving 820 patients met the eligibility criteria. A pooled mean follow-up time of 22.8 months (95% CI: 15.8, 29.9; I2 = 99.8%) was completed in 98.3% of patients. Pooled endoscopic recurrence was reported in 24.1% of patients (95% CI: 9.4, 49.3; I2 = 93.43%), pooled surgical recurrence in 3.9% of patients (95% CI: 2.2, 6.9; I2 = 25.97%), and pooled clinical recurrence in 26.8% of patients (95% CI: 14, 45.1; I2 = 84.87%). The pooled complication rate was 33.7%. The most common complications were infection (11.5%) and ileus (10.9%). Pooled anastomosis leakage rate was 2.9%.

CONCLUSIONS

Despite limited and heterogenous data, patients undergoing Kono-S anastomosis had low rates of surgical recurrence and anastomotic leakage with moderate rates of endoscopic recurrence, clinical recurrence and complications rate.

Panretinal handheld OCT angiography for pediatric retinal imaging

Comprehensive visualization of retina morphology is essential in the diagnosis and management of retinal diseases in pediatric populations. Conventional imaging techniques often face challenges in effectively capturing the peripheral retina, primarily due to the limitations in current optical designs, which lack the necessary field of view to characterize the far periphery. To address this gap, our study introduces a novel ultra-widefield optical coherence tomography angiography (OCTA) system. This system, specifically tailored for pediatric applications, incorporates an ultrahigh-speed 800 kHz swept-source laser. The system's innovative design achieves a 140° field of view while maintaining excellent optical performance. Over the last 15 months, we have conducted 379 eye examinations on 96 babies using this system. It demonstrates marked efficacy in the diagnosis of retinopathy of prematurity, providing detailed and comprehensive peripheral retinal angiography. The capabilities of the ultra-widefield handheld OCTA system in enhancing the clarity and thoroughness of retina vascularization assessments have significantly improved the precision of diagnoses and the customization of treatment strategies. Our findings underscore the system's potential to advance pediatric ophthalmology and broaden the scope of retinal imaging.

DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis

Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering enhanced structural and functional information (blood flow) with a wider field of view. This paper investigates automatic DR severity assessment using 3-D OCTA. A straightforward solution to this task is a 3-D neural network classifier. However, 3-D architectures have numerous parameters and typically require many training samples. A lighter solution consists in using 2-D neural network classifiers processing 2-D en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an approach mimics the way ophthalmologists analyze OCTA acquisitions: (1) en-face flow maps are often used to detect avascular zones and neovascularization, and (2) cross-sectional slices are commonly analyzed to detect macular edemas, for instance. However, arbitrary data reduction or selection might result in information loss. Two complementary strategies are thus proposed to optimally summarize OCTA volumes with 2-D images: (1) a parametric en-face projection optimized through deep learning and (2) a cross-sectional slice selection process controlled through gradient-based attribution. The full summarization and DR classification pipeline is trained from end to end. The automatic 2-D summary can be displayed in a viewer or printed in a report to support the decision. We show that the proposed 2-D summarization and classification pipeline outperforms direct 3-D classification with the advantage of improved interpretability.

Optical projection tomography reconstruction with few views using highly-generalizable deep learning at sinogram domain

Optical projection tomography (OPT) reconstruction using a minimal number of measured views offers the potential to significantly reduce excitation dosage and greatly enhance temporal resolution in biomedical imaging. However, traditional algorithms for tomographic reconstruction exhibit severe quality degradation, e.g., presence of streak artifacts, when the number of views is reduced. In this study, we introduce a novel domain evaluation method which can evaluate the domain complexity, and thereby validate that the sinogram domain exhibits lower complexity as compared to the conventional spatial domain. Then we achieve robust deep-learning-based reconstruction with a feedback-based data initialization method at sinogram domain, which shows strong generalization ability that notably improves the overall performance for OPT image reconstruction. This learning-based approach, termed SinNet, enables 4-view OPT reconstructions of diverse biological samples showing robust generalization ability. It surpasses the conventional OPT reconstruction approaches in terms of peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, showing its potential for the augment of widely-used OPT techniques.

OCT-angiography based artificial intelligence-inferred fluorescein angiography for leakage detection in retina [Invited]

Optical coherence tomography angiography (OCTA) covers most functions of fluorescein angiography (FA) when imaging the retina but lacks the ability to depict vascular leakage. Based on OCTA, we developed artificial intelligence-inferred-FA (AI-FA) to delineate leakage in eyes with diabetic retinopathy (DR). Training data of 19,648 still FA images were prepared from FA-photo and videos of 43 DR eyes. AI-FA images were generated using a convolutional neural network. AI-FA images achieved a structural similarity index of 0.91 with corresponding real FA images in DR. The AI-FA generated from OCTA correctly depicted vascular occlusion and associated leakage with enough quality, enabling precise DR diagnosis and treatment planning. A combination of OCT, OCTA, and AI-FA yields more information than real FA with reduced acquisition time without risk of allergic reactions.

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.

Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions

Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. We carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art single-purpose methods or existing multi-task learning solutions. We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. We also construct three datasets for multiple structure detection, and part of these datasets with the source code and evaluation benchmark have been released for public access.

Association Between Fluid Volume in Inner Nuclear Layer and Visual Acuity in Diabetic Macular Edema

PURPOSE

In diabetic macular edema (DME), the correlation between visual acuity (VA) and central subfield thickness (CST) is weak. We hypothesize that fluid volume (FV) in the inner nuclear layer (INL) may correlate more strongly with VA.

DESIGN

Retrospective, cross-sectional study.

METHODS

One eye each of diabetic patients with DME was included. We measured intraretinal fluid volume that was detected by automated fluid detection algorithm on 3- × 3-mm optical coherence tomography angiogram volume scans. The detected fluid was subdivided into inner FV, bounded by the INL, and outer FV, the fluid between the outer border of INL to the ellipsoid zone.

RESULTS

We enrolled 125 patients with DME (60 women; mean age, 61 years). The mean detected inner FV was 0.013 mm³ in 109 eyes (87%). The mean detected outer FV was 0.042 mm³ in 124 eyes (99%). Univariate analysis demonstrated that the VA significantly correlated with the inner FV (P < .0001), whole macular FV (P = .010), and CST (P = .036). Multivariate analysis demonstrated that the inner FV was the only significant factor (β = -0.41, P = .004). These correlations were consistent when the treatment-naïve group (n = 33) and the eyes without previous laser treatments (n = 93) were analyzed separately. The area under the receiver operating characteristic curve of inner FV for VA of 20/32 or worse was significantly higher than that for CST (0.66 vs 0.54, P = .018).

CONCLUSIONS

The inner FV has a stronger association with VA than other OCT biomarkers in DME and may be more clinically useful.