Bibliometric analysis of ophthalmic OCT and OCT angiography research trends over the past 20 years.

PURPOSE: Optical coherence tomography (OCT) and OCT angiography (OCTA) are widely used in the diagnosis of ophthalmic diseases. This study aims to provide a comprehensive bibliometric analysis of ophthalmologic OCT and OCT angiography. METHODS: We retrieved publications on ophthalmic OCT and OCTA from 2003 to 2022 from the Web of Science Core Collection and used bibliometric tools to analyze and visualize the distribution, trend, and hotspots. RESULTS: In total, 20,817 articles written by 48,160 authors from 106 countries were selected. The number of publications has significantly increased. In the last two decades, the USA was the most productive country and received the highest citations. The most productive journal was Investigative Ophthalmology and Visual Science and received the highest number of citations. Moorfields Eye Hospital was the most productive institution. Bandello F published the most papers, while Spaide RF was the highest cited author. SPAIDE RF, 2011, AM J Ophthalmology was the most cited document. "OCT", "glaucoma" and "OCTA" were three hotspots in the last two decades. "Vessel density" and "deep learning" would be research hotspots in the future. CONCLUSION: The bibliometric analysis of ophthalmic OCT and OCTA research over the past two decades on keywords, authors, citations, hotspots and trends will provide global researchers with valuable information for future research and cooperation.

Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic.

BACKGROUND: Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups. METHODS: The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aß-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments. RESULTS: We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aß- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding. CONCLUSIONS: We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection. TRIAL REGISTRATION: The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23-09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Evaluating the Diagnostic Accuracy of a Portable, Motorized, and Remotely Controlled Slit Lamp Imaging Adaptor Prototype for Head-Mounted Displays.

Purpose: The purpose of this study was to validate the performance of a portable and remotely controlled slit lamp imaging adaptor. Methods: Twenty patients with anterior eye segment conditions participated in a randomized masked clinical trial. Imaging was performed using a Haag-Streit AG, BX 900 slit lamp biomicroscope and a new slit lamp prototype. Three ophthalmologists independently reviewed masked and randomized 2D images from both instruments and conducted physical eye examinations based on these images. Inter- and intra-grader reliability were assessed using kappa statistics, and sensitivity and specificity were determined with reference to the clinical eye examinations performed during the patients' visits. Results: The sensitivity and specificity of the evaluations with the prototype were 47.8% and 64.1%. Similarly, the evaluations from the conventional system obtained a sensitivity and specificity of 49.5% and 66.2%. The differences in the sensitivity and specificity between imaging modalities were not statistically significant (P > 0.05). The intra-grader reliability showed moderate to substantial agreement between the systems (κ = 0.522-0.708). The inter-grader reliability also indicated moderate agreement for the evaluations with the conventional system (κ = 0.552) and the prototype (κ = 0.474). Conclusions: This study presents a new prototype that exhibits diagnostic accuracy on par with conventional slit lamps and moderate reliability. Further studies with larger sample sizes are required to characterize the prototype's performance. However, its remote functionality and accessibility suggest the potential to extend eye care. Translational Relevance: The development of portable and remotely controlled eye imaging systems will enhance teleophthalmology services and broaden access to eye care at the primary care level.

Disorders with Ophthalmic and Thoracic Involvement.

A variety of systemic conditions involve the thorax and the eyes. While subtle or nonspecific eye symptoms can be the initial clinical manifestation of some disorders, there can be additional manifestations in the thorax that lead to a specific diagnosis and affect patient outcomes. For instance, the initial clinical manifestation of Sjögren syndrome is dry eye or xerophthalmia; however, the presence of Sjögren lung disease represents a fourfold increase in mortality. Likewise, patients with acute sarcoidosis can initially present with pain and redness of the eye from uveitis in addition to fever and parotitis. Nearly 90% of patients with sarcoidosis have thoracic involvement, and the ophthalmologic symptoms can precede the thoracic symptoms by several years in some cases. Furthermore, a diagnosis made in one system can result in the screening of other organs as well as prompt genetic evaluation and examination of family members, such as in the setting of Marfan syndrome or Ehlers-Danlos syndrome. Multimodality imaging, particularly CT and MRI, plays a vital role in identification and characterization of these conditions. While it is helpful for ophthalmologists to be knowledgeable about these conditions and their associations so that they can order the pertinent radiologic studies, it is also important for radiologists to use the clues from ophthalmologic examination in addition to imaging findings to suggest a specific diagnosis. Systemic conditions with thoracic and ophthalmologic manifestations can be categorized as infectious, inflammatory, autoimmune, neoplastic, or hereditary in origin. The authors describe a spectrum of these conditions based on their underlying cause. ©RSNA, 2024.

Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases.

The global prevalence of ocular surface diseases (OSDs), such as dry eyes, conjunctivitis, and subconjunctival hemorrhage (SCH), is steadily increasing due to factors such as aging populations, environmental influences, and lifestyle changes. These diseases affect millions of individuals worldwide, emphasizing the importance of early diagnosis and continuous monitoring for effective treatment. Therefore, we present a deep learning-enhanced imaging system for the automated, objective, and reliable assessment of these three representative OSDs. Our comprehensive pipeline incorporates processing techniques derived from dual-mode infrared (IR) and visible (RGB) images. It employs a multi-stage deep learning model to enable accurate and consistent measurement of OSDs. This proposed method has achieved a 98.7% accuracy with an F1 score of 0.980 in class classification and a 96.2% accuracy with an F1 score of 0.956 in SCH region identification. Furthermore, our system aims to facilitate early diagnosis of meibomian gland dysfunction (MGD), a primary factor causing dry eyes, by quantitatively analyzing the meibomian gland (MG) area ratio and detecting gland morphological irregularities with an accuracy of 88.1% and an F1 score of 0.781. To enhance convenience and timely OSD management, we are integrating a portable IR camera for obtaining meibography during home inspections. Our system demonstrates notable improvements in expanding dual-mode image-based diagnosis for broader applicability, effectively enhancing patient care efficiency. With its automation, accuracy, and compact design, this system is well-suited for early detection and ongoing assessment of OSDs, contributing to improved eye healthcare in an accessible and comprehensible manner.

Imaging performance of portable and conventional ultrasound imaging technologies for ophthalmic applications.

PURPOSE: The aim of this study was to evaluate the imaging capabilities of Butterfly iQ with conventional ophthalmic (piezoelectric) ultrasound (COU) for ophthalmic imaging. METHODS: Custom phantom molds were designed and imaged with Butterfly iQ and COU to compare spatial resolution capabilities. To evaluate the clinical imaging performance of Butterfly iQ and COU, a survey containing pathological conditions from human subjects, imaged with both Butterfly iQ and COU probes, was given to three retina specialists and graded on image detail, resolution, quality, and diagnostic confidence on a ten-point Likert scale. Kruskal-Wallis analysis was performed for survey responses. RESULTS: Butterfly iQ and COU had comparable capabilities for imaging small axial and lateral phantom features (down to 0.1 mm) of high and low acoustic reflectivity. One of three retina specialists demonstrated a statistically significant preference for COU related to resolution, detail, and diagnostic confidence, but the remaining graders showed no significant preference for Butterfly iQ or COU across all sample images presented. CONCLUSION: The emergence of portable ultrasound probes offers an affordable alternative to COU technologies with comparable qualitative imaging resolution down to 0.1 mm. These findings suggest the value to further study the use of portable ultrasound systems and their utility in routine eye care.

Multimodal imaging observation of primary vitreous cysts.

BACKGROUND: Primary vitreous cyst is a clinical variant delineated by the existence of a vesicle within the vitreous cavity from birth. This particular disease tends to be uncommon, and the underlying mechanisms contributing to its pathogenesis remain obscure. CASE PRESENTATION: A 37-year-old male patient manifested blurry vision and floaters in his right eye, a symptomology first noticed three months prior. Upon slit-lamp examination, a pigmented, round, 1 papilla diameter-sized mass was discerned floating in the vitreous. A meticulous examination of the floaters was conducted using an array of multimodal imaging techniques. Other potential conditions, including cysticercosis, toxoplasmosis, and tumors, were conclusively excluded through comprehensive diagnostic tests such as blood examinations, liver ultrasound, and cranial magnetic resonance imaging (MRI), resulting in the diagnosis of a primary vitreous cyst. The patient did not report any other discomforts and did not receive any subsequent interventions or treatments. CONCLUSION: We furnish an exhaustive case report of a patient diagnosed with a primary vitreous cyst. The incorporation of multimodal images in the characterization of the disease anticipates facilitating an enriched comprehension by medical practitioners.

Confidence-aware multi-modality learning for eye disease screening.

Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information using a multi-distribution fusion perspective. Specifically, our method first utilizes normal inverse gamma prior distributions over pre-trained models to learn both aleatoric and epistemic uncertainty for uni-modality. Then, the normal inverse gamma distribution is analyzed as the Student's t distribution. Furthermore, within a confidence-aware fusion framework, we propose a mixture of Student's t distributions to effectively integrate different modalities, imparting the model with heavy-tailed properties and enhancing its robustness and reliability. More importantly, the confidence-aware multi-modality ranking regularization term induces the model to more reasonably rank the noisy single-modal and fused-modal confidence, leading to improved reliability and accuracy. Experimental results on both public and internal datasets demonstrate that our model excels in robustness, particularly in challenging scenarios involving Gaussian noise and modality missing conditions. Moreover, our model exhibits strong generalization capabilities to out-of-distribution data, underscoring its potential as a promising solution for multimodal eye disease screening.

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

BACKGROUND: Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. METHODS: In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations. RESULTS: We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy. CONCLUSION: The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.

Light Scattering by Vitreous of Humans With Vision Degrading Myodesopsia From Floaters.

Purpose: Vision-degrading myodesopsia (VDM) from vitreous floaters significantly degrades vision and impacts visual quality of life (VQOL), but the relationship to light scattering is poorly understood. This study compared in vitro measures of light scatter and transmission in surgically excised human vitreous to preoperative indexes of vitreous structure, visual function, and VQOL. Methods: Pure vitreous collected during vitrectomy from 8 patients with VDM had wide-angle straylight measurements and dark-field imaging, performed within 36 hours of vitrectomy. Preoperative VQOL assessment with VFQ-25, contrast sensitivity (CS) measurements with Freiburg acuity contrast testing, and quantitative ultrasonography were compared to light scattering and transmission in vitro. Results: All indices of vitreous echodensity in vivo correlated positively with straylight at 0.5° (R = 0.708 to 0.775, P = 0.049 and 0.024, respectively). Straylight mean scatter index correlated with echodensity (R = 0.71, P = 0.04) and VQOL (R = -0.82, P = 0.0075). Dark-field measures in vitro correlated with degraded CS in vivo (R = -0.69, P = 0.04). VQOL correlated with straylight mean scatter index (R = -0.823, P = 0.012). Conclusions: Increased vitreous echodensity in vivo is associated with more straylight scattering in vitro, validating ultrasonography as a clinical surrogate for light scattering. Contrast sensitivity in vivo is more degraded in the presence of dark-field scattering in vitro and VQOL is decreased in patients whose vitreous has increased light scattering. These findings could form the basis for the development of optical corrections for VDM or support new laser treatments, as well as novel pharmacotherapy.

Thermal mapping the eye: A critical review of advances in infrared imaging for disease detection.

Infrared thermography (IRT) has become more accessible due to technological advancements, making thermal cameras more affordable. Infrared thermal cameras capture the infrared rays emitted by objects and convert it into temperature representations. IRT has emerged as a promising and non-invasive approach for examining the human eye. Ocular surface temperature assessment based on IRT is vital for the diagnosis and monitoring of various eye conditions like dry eye, diabetic retinopathy, glaucoma, allergic conjunctivitis, and inflammatory diseases. A collective sum of 192 articles was sourced from various databases, and through adherence to the PRISMA guidelines, 29 articles were ultimately chosen for systematic analysis. This systematic review article seeks to provide readers with a thorough understanding of IRT's applications, advantages, limitations, and recent developments in the context of eye examinations. It covers various aspects of IRT-based eye analysis, including image acquisition, processing techniques, ocular surface temperature measurement, three different approaches to identifying abnormalities, and different evaluation metrics used. Our review also delves into recent advancements, particularly the integration of machine learning and deep learning algorithms into IRT-based eye examinations. Our systematic review not only sheds light on the current state of research but also outlines promising future prospects for the integration of infrared thermography in advancing eye health diagnostics and care.

Optical coherence tomography angiography in the diagnosis of ocular disease.

Clinical imaging provided by optical coherence tomography (OCT) and its variant, OCT-angiography (OCT-A), has revolutionised eyecare practice. The imaging techniques allow for the identification and quantification of ocular structures, supporting the diagnosis and prognosis of eye disease. In this review, an overview of the usefulness of OCT-A imaging in the diagnosis and management of a range of ocular conditions is provided when used in isolation or in combination with other imaging modalities and measures of visual function (visual field results). OCT-A imaging has the capacity to identify and quantify ocular vasculature non-invasively, thereby assisting the clinician in the diagnosis or to determine the efficacy of intervention in ocular conditions impacting retinal vasculature. Thus, additional clinically useful information can be obtained in eye diseases involving conditions such as those impacting retinal vessel occlusion, in diabetic retinopathy, inherited retinal dystrophy, age-related macular degeneration, choroidal neovascularisation and optic nerve disorders. Through a clinical case series, various ocular conditions are reviewed, and the impact of OCT-A imaging is discussed. Although OCT-A imaging has great promise and is already used in clinical management, there is a lack of set standards to characterise altered vascular features in disease and consequently for prognostication, primarily due to a lack of large-scale clinical trials and variability in OCT-A algorithms when generating quantitative parameters.

Pediatric orbital lesions: ocular pathologies.

Orbital pathologies can be broadly classified as ocular, extra-ocular soft-tissue (non-neoplastic and neoplastic), osseous, and traumatic. In part 1 of this orbital series, the authors will discuss the differential diagnosis and key imaging features of pediatric ocular pathologies. These include congenital and developmental lesions (microphthalmos, anophthalmos, persistent fetal vasculature, coloboma, morning glory disc anomaly, retinopathy of prematurity, Coats disease), optic disc drusen, infective and inflammatory lesions (uveitis, toxocariasis, toxoplasmosis), and ocular neoplasms (retinoblastoma, retinal hamartoma, choroidal melanoma, choroidal nevus). This pictorial review provides a practical approach to the imaging work-up of these anomalies with a focus on ocular US as the first imaging modality and additional use of CT and/or MRI for the evaluation of intracranial abnormalities. The characteristic imaging features of the non-neoplastic mimics of retinoblastoma, such as persistent fetal vasculature and Coats disease, are also highlighted.

AUTOMATED DETECTION OF VITRITIS USING ULTRAWIDE-FIELD FUNDUS PHOTOGRAPHS AND DEEP LEARNING.

BACKGROUND/PURPOSE: Evaluate the performance of a deep learning algorithm for the automated detection and grading of vitritis on ultrawide-field imaging. METHODS: Cross-sectional noninterventional study. Ultrawide-field fundus retinophotographs of uveitis patients were used. Vitreous haze was defined according to the six steps of the Standardization of Uveitis Nomenclature classification. The deep learning framework TensorFlow and the DenseNet121 convolutional neural network were used to perform the classification task. The best fitted model was tested in a validation study. RESULTS: One thousand one hundred eighty-one images were included. The performance of the model for the detection of vitritis was good with a sensitivity of 91%, a specificity of 89%, an accuracy of 0.90, and an area under the receiver operating characteristics curve of 0.97. When used on an external set of images, the accuracy for the detection of vitritis was 0.78. The accuracy to classify vitritis in one of the six Standardization of Uveitis Nomenclature grades was limited (0.61) but improved to 0.75 when the grades were grouped into three categories. When accepting an error of one grade, the accuracy for the six-class classification increased to 0.90, suggesting the need for a larger sample to improve the model performances. CONCLUSION: A new deep learning model based on ultrawide-field fundus imaging that produces an efficient tool for the detection of vitritis was described. The performance of the model for the grading into three categories of increasing vitritis severity was acceptable. The performance for the six-class grading of vitritis was limited but can probably be improved with a larger set of images.