Ophthalmologic Imaging Research Articles

Artificial intelligence applications in ophthalmic surgery.

Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient. Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures. The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.

DEVELOPMENT OF AN EXPERT SYSTEM BASED ON FUZZY LOGIC FOR EARLY DIAGNOSIS OF GLAUCOMA

Nowadays, decision-making systems that rely on images are becoming increasingly crucial, especially in the medical field. Images have become a fundamental tool for clinical research and diagnosing illnesses. In the case of glaucoma, a disease that can damage the optic nerve head and result in irreversible vision loss, a new Fuzzy Expert System has been developed for early diagnosis. Original ONH images are preprocessed with filters to remove noise, followed by using the Canny detector algorithm to detect contours. Key parameters are then extracted by identifying elliptical forms of the optic disc and excavation using the Randomized Hough Transform. A classification algorithm based on fuzzy logic is used to assess patients' conditions, taking into account both instrumental parameters and risk factors such as age, race, and family history. The system is tested on a dataset of ophthalmologic images, showing a significant improvement in predictions compared to existing methods, with over 96% accuracy in identifying cases suspected to have glaucoma.

Implementation and Execution of Big Data-based Studies in Ophthalmology within the Framework of the GDPR.

The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. "Big Data" enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements.

Depression and Eye Disease-A Narrative Review of Common Underlying Pathophysiological Mechanisms and their Potential Applications.

Background: Depression has been shown to be associated with eye diseases, including dry eye disease (DED), cataracts, glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR). This narrative review explores potential pathophysiological connections between depression and eye disease, as well as its potential correlations with ocular parameters. Methods: A literature search was conducted in August 2022 in PUBMED, EMBASE, and PsycINFO. Published articles related to the subject were consolidated and classified according to respective eye diseases and pathophysiological mechanisms. Results: The literature reviewed suggests that common pathophysiological states like inflammation and neurodegeneration may contribute to both depression and certain eye diseases, while somatic symptoms and altered physiology, such as disruptions in circadian rhythm due to eye diseases, can also influence patients' mood states. Grounded in the shared embryological, anatomical, and physiological features between the eye and the brain, depression is also correlated to changes observed in non-invasive ophthalmological imaging modalities, such as changes in the retinal nerve fibre layer and retinal microvasculature. Conclusions: There is substantial evidence of a close association between depression and eye diseases. Understanding the underlying concepts can inform further research on treatment options and monitoring of depression based on ocular parameters.

Expanding the phenotypic spectrum of NOTCH1 variants: clinical manifestations in families with congenital heart disease.

Pathogenic variants in NOTCH1 are associated with non-syndromic congenital heart disease (CHD) and Adams-Oliver syndrome (AOS). The clinical presentation of individuals with damaging NOTCH1 variants is characterized by variable expressivity and incomplete penetrance; however, data on systematic phenotypic characterization are limited. We report the genotype and phenotype of a cohort of 33 individuals (20 females, 13 males; median age 23.4 years, range 2.5-68.3 years) from 11 families with causative NOTCH1 variants (9 inherited, 2 de novo; 9 novel), ascertained from a proband with CHD. We describe the cardiac and extracardiac anomalies identified in these 33 individuals, only four of whom met criteria for AOS. The most common CHD identified was tetralogy of Fallot, though various left- and right-sided lesions and septal defects were also present. Extracardiac anomalies identified include cutis aplasia (5/33), cutaneous vascular anomalies (7/33), vascular anomalies of the central nervous system (2/10), Poland anomaly (1/33), pulmonary hypertension (2/33), and structural brain anomalies (3/14). Identification of these findings in a cardiac proband cohort supports NOTCH1-associated CHD and NOTCH1-associated AOS lying on a phenotypic continuum. Our findings also support (1) Broad indications for NOTCH1 molecular testing (any familial CHD, simplex tetralogy of Fallot or hypoplastic left heart); (2) Cascade testing in all at-risk relatives; and (3) A thorough physical exam, in addition to cardiac, brain (structural and vascular), abdominal, and ophthalmologic imaging, in all gene-positive individuals. This information is important for guiding the medical management of these individuals, particularly given the high prevalence of NOTCH1 variants in the CHD population.

Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

Visualization Materials Using Silicon-Based Optical Nanodisks (ViSiON) for Enhanced NIR Imaging in Ophthalmology.

ViSiON (visualization materials composed of silicon-based optical nanodisks) is presented, which offers a unique optical combination of near-infrared (NIR) optical properties and biodegradability. Initially, numerical simulations are conducted to calculate the total extinction and scattering effects of ViSiON by the diameter-to-thickness ratio, predicting precise control over its scattering properties in the NIR region. A top-down patterning technique is employed to synthesize ViSiON with accurate diameter and thickness control. ViSiON with a 50nm thickness exhibits scattering properties over 400 times higher than that of 30nm, rendering it suitable as a contrast agent for optical coherence tomography (OCT), especially in ophthalmic applications. Furthermore, ViSiON possesses inherent biodegradability in media, with ≈95% degradation occurring after 48 h, and the degradation rate can be finely tuned based on the quantity of protein coating applied to the surface. Subsequently, the OCT imaging capability is validated even within vessels smaller than 300µm, simulating retinal vasculature using a retinal phantom. Then, using an ex ovo chick embryo model, it is demonstrated that ViSiON enhances the strength of protein membranes by 6.17 times, thereby presenting the potential for ViSiON as an OCT imaging probe capable of diagnosing retinal diseases.

Current roles of artificial intelligence in ophthalmology

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations.

Molecular and cellular imaging of the eye.

The application of molecular and cellular imaging in ophthalmology has numerous benefits. It can enable the early detection and diagnosis of ocular diseases, facilitating timely intervention and improved patient outcomes. Molecular imaging techniques can help identify disease biomarkers, monitor disease progression, and evaluate treatment responses. Furthermore, these techniques allow researchers to gain insights into the pathogenesis of ocular diseases and develop novel therapeutic strategies. Molecular and cellular imaging can also allow basic research to elucidate the normal physiological processes occurring within the eye, such as cell signaling, tissue remodeling, and immune responses. By providing detailed visualization at the molecular and cellular level, these imaging techniques contribute to a comprehensive understanding of ocular biology. Current clinically available imaging often relies on confocal microscopy, multi-photon microscopy, PET (positron emission tomography) or SPECT (single-photon emission computed tomography) techniques, optical coherence tomography (OCT), and fluorescence imaging. Preclinical research focuses on the identification of novel molecular targets for various diseases. The aim is to discover specific biomarkers or molecular pathways associated with diseases, allowing for targeted imaging and precise disease characterization. In parallel, efforts are being made to develop sophisticated and multifunctional contrast agents that can selectively bind to these identified molecular targets. These contrast agents can enhance the imaging signal and improve the sensitivity and specificity of molecular imaging by carrying various imaging labels, including radionuclides for PET or SPECT, fluorescent dyes for optical imaging, or nanoparticles for multimodal imaging. Furthermore, advancements in technology and instrumentation are being pursued to enable multimodality molecular imaging. Integrating different imaging modalities, such as PET/MRI (magnetic resonance imaging) or PET/CT (computed tomography), allows for the complementary strengths of each modality to be combined, providing comprehensive molecular and anatomical information in a single examination. Recently, photoacoustic microscopy (PAM) has been explored as a novel imaging technology for visualization of different retinal diseases. PAM is a non-invasive, non-ionizing radiation, and hybrid imaging modality that combines the optical excitation of contrast agents with ultrasound detection. It offers a unique approach to imaging by providing both anatomical and functional information. Its ability to utilize molecularly targeted contrast agents holds great promise for molecular imaging applications in ophthalmology. In this review, we will summarize the application of multimodality molecular imaging for tracking chorioretinal angiogenesis along with the migration of stem cells after subretinal transplantation in vivo.

Resurfacing Dehiscence(s) Without Reducing Diverticulum Effectively Silences Pulsatile Tinnitus: Novel Surgical Techniques for Diverticulum and Intraoperative Microphone Monitoring.

To emphasize the surgical importance of addressing dehiscence over diverticulum in resolving pulsatile tinnitus (PT) in patients with sigmoid sinus wall anomalies (SSWAs) and investigate anatomical differences. Retrospective data analysis. Multi-institutional tertiary university medical centers. Fifty participants (dehiscence/diverticulum, 29:21 cases) with SSWA-associated PT were included in the study. All 21 diverticulum participants underwent surgical intervention. 1) Surgical intervention with novel techniques monitored by intraoperative microphone. 2) Radiologic and ophthalmologic imaging methods. Quantitative and qualitative preoperative and postoperative alterations of PT and anatomical differences between dehiscence and diverticulum. Addressing dehiscence overlying diverticulum and sigmoid sinus wall dehiscences significantly reduced visual analog score and Tinnitus Handicap Inventory ( p < 0.01). Sinus wall reconstruction led to substantial PT sound intensity reduction in the frequency range of 20 to 1000 Hz and 20 to 500 Hz (paired-sample t test, p < 0.01). Diploic vein analysis showed a significant positive correlation in 85.7% of the diverticulum cohort compared with the dehiscence cohort ( p < 0.01). Eight percent of the participants exhibited papilledema, which was limited to the dehiscence cohort. 1) Effective reduction of PT can be achieved by addressing all dehiscences, including those overlying the diverticulum, without the need to exclude the diverticulum. 2) Diploic vein may involve in the formation of diverticulum, and loss of dura mater and vascular wall thickness are observed at the SSWA locations.

Ophthalmic Imaging in India - Past, Present and Future

Ophthalmic imaging has shown tremendous progress in the past century in terms of numerous advancements in the way we image and interpret the human eye, with all its intricacies. Recent times have emphasized imaging as a diagnostic tool, a modality to document patient progress, and a useful learning medium for budding clinicians. Imaging in ophthalmology is no longer as unidimensional as it used to be. It has expanded from simple slit lamp examination, which functioned on lights and mirrors, to now involve augmented reality technology with its four-dimensional capabilities, completely changing the course of how we diagnose ocular pathologies. India, as a country, has shown a steady pace in incorporating ocular imaging advancements into her regular ophthalmic practice, and there is hope that the country has massive purview in bringing in more technological betterments in the days to come.

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

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. 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. 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. 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.

An Update on Multimodal Ophthalmological Imaging of Diffuse Choroidal Hemangioma in Sturge-Weber Syndrome.

Sturge-Weber syndrome (SWS) is characterized by facial port-wine stains, leptomeningeal hemangiomas, and prominent ocular manifestations such as glaucoma and diffuse choroidal hemangiomas (DCHs). Imaging modalities are critical for diagnosing and longitudinally monitoring DCHs in SWS. Fundus photography is fundamental in assessing both eyes simultaneously, fluorescein angiography and indocyanine green angiography effectively map the retinal and choroidal circulation, and ultrasonography offers essential structural insights into the choroid and retina. NIR imaging reveals subtle retinal pigment changes, often overlooked in standard fundus examination. Enhanced depth imaging spectral domain optical coherence tomography (EDI-SDOCT) and swept-source OCT (SSOCT) improve the visualization of the choroidal-scleral boundary, essential for DCH characterization. The potential of OCT angiography (OCTA) is under exploration, particularly its role in predicting signs of disease progression or worsening, as well as potential new biomarkers such as the choroidal vascularity index (CVI). The present review aims to provide an update on multimodal imaging of DCHs in SWS.

Insights into Novel Choroidal and Retinal Clinical Signs in Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is a rare inherited neurocutaneous disorder with a major impact on the skin, nervous system and eyes. The ocular diagnostic hallmarks of this disease include iris Lisch nodules, ocular and eyelid neurofibromas, eyelid café-au-lait spots and optic pathway gliomas (OPGs). In the last years, new manifestations have been identified in the ocular district in NF1 including choroidal abnormalities (CAs), hyperpigmented spots (HSs) and retinal vascular abnormalities (RVAs). Recent advances in multi-modality imaging in ophthalmology have allowed for the improved characterization of these clinical signs. Accordingly, CAs, easily detectable as bright patchy nodules on near-infrared imaging, have recently been added to the revised diagnostic criteria for NF1 due to their high specificity and sensitivity. Furthermore, subclinical alterations of the visual pathways, regardless of the presence of OPGs, have been recently described in NF1, with a primary role of neurofibromin in the myelination process. In this paper, we reviewed the latest progress in the understanding of choroidal and retinal abnormalities in NF1 patients. The clinical significance of the recently revised diagnostic criteria for NF1 is discussed along with new updates in molecular diagnosis. New insights into NF1-related neuro-ophthalmic manifestations are also provided based on electrophysiological and optical coherence tomography (OCT) studies.

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS).

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying etiology of SANS is not well understood. Current ophthalmic imaging onboard the International Space Station (ISS) has provided further insights into SANS. However, the spaceflight environment presents with unique challenges and limitations to further understand this microgravity-induced phenomenon. The advent of artificial intelligence (AI) has revolutionized the field of imaging in ophthalmology, particularly in detection and monitoring. In this manuscript, we describe the current hypothesized pathophysiology of SANS and the medical diagnostic limitations during spaceflight to further understand its pathogenesis. We then introduce and describe various AI frameworks that can be applied to ophthalmic imaging onboard the ISS to further understand SANS including supervised/unsupervised learning, generative adversarial networks, and transfer learning. We conclude by describing current research in this area to further understand SANS with the goal of enabling deeper insights into SANS and safer spaceflight for future missions.

Achieving rapid and significant results in healthcare services by using the theory of constraints

ABSTRACT Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation – which included the drum-buffer-rope scheduling and buffer management – and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.

Structural abnormalities of choroid vasculature in first-episode psychosis

IntroductionThe eye is considered as a part of the central nervous system and a window of the brain. The eye and the brain, also their microvascular system, share many common features in terms of anatomy and pathophysiology. Choroid and retina related abnormalities have been reported in psychotic disorders, especially in schizophrenia. Choroidal vascularity index (CVI) is a useful tool to assess choroidal structural alterations and CVI is defined as the proportion of luminal area (LA) to the total choroidal area (TCA).ObjectivesThis is the first study to investigate choroidal vascularity index in first-episode psychosis (FEP) using optical coherence tomography (OCT).Methods31 patients with FEP and 30 age and gender-matched healthy controls (HC) included in this study. All participants underwent psychiatric and ophthalmological clinical assessments. Imaging of the choroid was performed using enhanced deep imaging (EDI) protocol - spectral domain OCT. Central choroidal thickness (CCT) with EDI-OCT was measured manually from the inner border of the sclera to the outer border of the RPE vertically using the calipers of the Heidelberg reader subfoveally. The choroidal vascularity index was assessed using the EDI-OCT images.ResultsThere were no statistically significant differences between FEP and HC in terms of CCT (p=0.33), TCA (p=0.809) and LCA (p=0.710). The CVIs were 66.66±2.98% and 62.32±3.32% in FEP and HC, respectively and it was significantly different between groups (p&lt;0.001, effect size d=1.38).ConclusionsThese results suggest that first episode psychosis is characterized by choroidal vascular abnormalities. in vivo ophthalmological imaging with OCT, a non-invasive, fast, and safe technological tool, can be a promising biomarker to quantitatively evaluate microvasculature abnormalities in patients with psychosis.Disclosure of InterestNone Declared

Primary open-angle glaucoma in patients with obstructive sleep apnoea in a Colombian population: a cross-sectional study

ObjectiveDetermine the prevalence, functional and structural alterations of primary open-angle glaucoma (POAG) in patients with obstructive sleep apnoea (OSA).DesignCross-sectional.SettingTertiary hospital associated with specialised center in ophthalmologic images in Bogota, Colombia.Participants150...

Knowledge domain and dynamic patterns in multimodal molecular imaging from 2012 to 2021: A visual bibliometric analysis.

Multimodal molecular imaging technologies have been widely used to optimize medical research and clinical practice. Bibliometric analysis was performed to identify global research trends, hot spots, and scientific frontiers of multimodal molecular imaging technology from 2012 to 2021. The articles and reviews related to multimodal molecular imaging were retrieved from the Web of Science Core Collection. A bibliometric study was performed using CiteSpace and VOSviewer. A total of 4169 articles and reviews from 2012 to 2021 were analyzed. An increasing trend in the number of articles on multimodal molecular imaging technology was observed. These publications mainly come from 417 institutions in 92 countries, led by the USA and China. K. Bailey Freund published the most papers amongst the publications, while R.F. Spaide had the most co-citations. A dual map overlay of the literature shows that most publications were specialized in physics/materials/chemistry, and molecular/biology/immunology. Synergistic therapy in cancer, advanced nanotechnology, and multimodal imaging in ophthalmology are new trends and developing areas of interest. A global bibliometric and visualization analysis was used to comprehensively review the published research related to multimodal molecular imaging. This study may help in understanding the dynamic patterns of multimodal molecular imaging technology research and point out the developing areas of this field.

Increased ocular wall thickness and decreased globe volume in children with mucopolysaccharidosis type VI.

PURPOSE lthough clinical ophthalmologic findings have been reported, no study documented magnetic resonance imaging (MRI) findings in mucopolysaccharidosis (MPS) type VI. The aim of this study was to determine the ophthalmologic imaging findings of MPS type VI in the pediatric age group retrospectively. METHODS Brain MRIs of 10 patients with MPS type VI and 49 healthy children were evaluated independently by two pediatric radiologists for the following characteristics: globe volume, ocular wall thickness, and optic nerve sheath diameter for each orbit. The means of the measurement of each group were compared by using an independent t-test. Agreement and bias between reviewers were assessed by intra-class correlation coefficients (ICC). RESULTS A total of 59 children [32 girls (54.23%), 27 boys (45.77%); age range, 4-16 years; mean age, 10.37 ± 3.73 years] were included in the study. Statistical analysis revealed smaller eyeballs and thicker ocular walls of patients with MPS type VI (P < .001 and P < .001, respectively). However, there was no statistically significant difference in terms of optic nerve sheath diameter between the two groups (P=.648). CONCLUSION Patients with MPS type VI displayed reduced globe volumes and increased ocular wall thicknesses compared to the healthy children. Therefore, we recommend that ophthalmologic imaging findings might prove to be an auxiliary tool in the diagnosis of MPS patients.