Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization.

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

Optical coherence tomography biomarkers for myopic choroidal neovascularization treated with anti-vascular endothelial growth factor.

In recent years, optical coherence tomography (OCT) biomarkers for specific retinal diseases have been found to be associated with treatment outcome and disease recurrence. The main purposes of this study were to identify OCT biomarkers for myopic choroidal neovascularization (mCNV) treated with intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF). OCT features in 43 eyes of 39 patients with mCNV treated with anti-VEGF with at least 1 year of follow-up were retrospectively analyzed. Eyes with subretinal hyperreflective material (SHM) in baseline spectral-domain OCT (SD-OCT) had significantly more visual improvement than eyes without SHM at month 6 (p = 0.007) and had a trend of more visual improvement than eyes without SHM (p = 0.058) at month 12. Eyes with subretinal fluid (SRF) at baseline had significantly more central retinal thickness (CRT) decrease than patients without SRF at month 6 and 12 (p = 0.012 and 0.006 respectively). In univariate regression analysis, dome-shaped macula (DSM), SRF in baseline OCT image and fuzzy border of mCNV when entering pro re nata (PRN) injection protocol tended to have higher risk of disease recurrence in 1 year (odds ratio: 14.86 (p = 0.003), 3.75 (p = 0.049) and 22.92 (p < 0.001) respectively). However, they were not significant in multivariate regression analysis. OCT biomarkers at baseline could provide prognostic information for mCNV management.

A deep network using coarse clinical prior for myopic maculopathy grading.

Pathological Myopia (PM) is a globally prevalent eye disease which is one of the main causes of blindness. In the long-term clinical observation, myopic maculopathy is a main criterion to diagnose PM severity. The grading of myopic maculopathy can provide a severity and progression prediction of PM to perform treatment and prevent myopia blindness in time. In this paper, we propose a feature fusion framework to utilize tessellated fundus and the brightest region in fundus images as prior knowledge. The proposed framework consists of prior knowledge extraction module and feature fusion module. Prior knowledge extraction module uses traditional image processing methods to extract the prior knowledge to indicate coarse lesion positions in fundus images. Furthermore, the prior, tessellated fundus and the brightest region in fundus images, are integrated into deep learning network as global and local constrains respectively by feature fusion module. In addition, rank loss is designed to increase the continuity of classification score. We collect a private color fundus dataset from Beijing TongRen Hospital containing 714 clinical images. The dataset contains all 5 grades of myopic maculopathy which are labeled by experienced ophthalmologists. Our framework achieves 0.8921 five-grade accuracy on our private dataset. Pathological Myopia (PALM) dataset is used for comparison with other related algorithms. Our framework is trained with 400 images and achieves an AUC of 0.9981 for two-class grading. The results show that our framework can achieve a good performance for myopic maculopathy grading.

Pathologic myopia: advances in imaging and the potential role of artificial intelligence.

Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.

An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.

Purpose: To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions. Methods: Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation-based co-decision model. Results: This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230. Conclusions: The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions. Translational Relevance: The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

Longitudinal changes of circumpapillary retinal nerve fiber layer thickness profile during childhood myopia progression.

The purpose of this study was to evaluate longitudinal changes of circumpapillary retinal nerve fiber layer thickness (cpRNFLT) profile arising in the course of childhood myopia progression. Thirty-six eyes of 36 healthy children who showed myopia progression (spherical equivalent [SE] decrease of ≥ 2.0 diopters [D]) were included. To account for the axial-elongation-induced magnification effect on spectral-domain optical coherence tomography (SD-OCT) measurements, we calculated the proportion of quadrant-cpRNFLT distribution (i.e., the percentage of cpRNFLT within a single quadrant of total cpRNFLT). During 4.1 ± 1.1 years, the mean SE changed from -1.3 ± 0.9 to -4.3 ± 0.8D, and both the optic disc tilt ratio and the torsional angle increased (both P < 0.001). In the temporal quadrant, the cpRNFLT proportion was increased from 19.2 ± 1.86 to 24.4 ± 2.30% (P < 0.001). The cpRNFLT proportion in 3 quadrants (i.e., superior, inferior, nasal) showed decreases (all P < 0.001). Between baseline and follow up, the scan-circle location as determined by OCT was shifted mostly (94%; 34 of 36 eyes) toward the nasal side of the optic disc. With scan-circle repositioning to match the baseline, cpRNFLT distribution proportions did not show any significant difference between the baseline and follow up (all P > 0.05). For longitudinal evaluations of patients with myopia progression, scan-circle alteration should be given due consideration.

Validation of Soft Labels in Developing Deep Learning Algorithms for Detecting Lesions of Myopic Maculopathy From Optical Coherence Tomographic Images.

PURPOSE: It is common for physicians to be uncertain when examining some images. Models trained with human uncertainty could be a help for physicians in diagnosing pathologic myopia. DESIGN: This is a hospital-based study that included 9176 images from 1327 patients that were collected between October 2015 and March 2019. METHODS: All collected images were graded by 21 myopia specialists according to the presence of myopic neovascularization (MNV), myopic traction maculopathy (MTM), and dome-shaped macula (DSM). Hard labels were made by the rule of major wins, while soft labels were possibilities calculated by whole grading results from the different graders. The area under the curve (AUC) of the receiver operating characteristics curve, the area under precision-recall (AUPR) curve, F-score, and least square errors were used to evaluate the performance of the models. RESULTS: The AUC values of models trained by soft labels in MNV, MTM, and DSM models were 0.985, 0.946, and 0.978; and the AUPR values were 0.908, 0.876, and 0.653 respectively. However, 0.56% of MNV "negative" cases were answered as "positive" with high certainty by the hard label model, whereas no case was graded with extreme errors by the soft label model. The same results were found for the MTM (0.95% vs none) and DSM (0.43% vs 0.09%) models. CONCLUSIONS: The predicted possibilities from the models trained by soft labels were close to the results made by myopia specialists. These findings could inspire the novel use of deep learning models in the medical field.

Longitudinal Macular Retinal and Choroidal Microvasculature Changes in High Myopia.

Purpose: The purpose of this study was to determine the longitudinal changes in macular retinal and choroidal microvasculature in normal healthy and highly myopic eyes. Methods: Seventy-one eyes, including 32 eyes with high myopia and 39 healthy control eyes, followed for at least 12 months and examined using optical coherence tomography angiography imaging in at least 3 visits, were included in this study. Fovea-centered 6 × 6 mm scans were performed to measure capillary density (CD) of the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC). The rates of CD changes in both groups were estimated using a linear mixed model. Results: Over a mean 14-month follow-up period, highly myopic eyes exhibited a faster rate of whole image CD (wiCD) loss (-1.44%/year vs. -0.11%/year, P = 0.001) and CD loss in the outer ring of the DCP (-1.67%/year vs. -0.14%/year, P < 0.001) than healthy eyes. In multivariate regression analysis, baseline axial length (AL) was negatively correlated with the rate of wiCD loss (estimate = -0.27, 95% confidence interval [CI] = -0.48 to -0.06, P = 0.012) and CD loss in the outer ring (estimate = -0.33, 95% CI = -0.56 to -0.11, P = 0.005), of the DCP. The CD reduction rates in the SCP and CC were comparable in both groups (all P values > 0.05). Conclusions: The rate of CD loss in the DCP is significantly faster in highly myopic eyes than in healthy eyes and is related to baseline AL. The CD in the outer ring reduces faster in eyes with longer baseline AL.

Automatic detection of pathological myopia using machine learning.

Pathological myopia is a severe case of myopia, i.e., nearsightedness. Pathological myopia is also known as degenerative myopia because it ultimately leads to blindness. In pathological myopia, certain myopia-specific pathologies occur at the eye's posterior i.e., Foster-Fuchs's spot, Cystoid degeneration, Liquefaction, Macular degeneration, Vitreous opacities, Weiss's reflex, Posterior staphyloma, etc. This research is aimed at developing a machine learning (ML) approach for the automatic detection of pathological myopia based on fundus images. A deep learning technique of convolutional neural network (CNN) is employed for this purpose. A CNN model is developed in Spyder. The fundus images are first preprocessed. The preprocessed images are then fed to the designed CNN model. The CNN model automatically extracts the features from the input images and classifies the images i.e., normal image or pathological myopia. The best performing CNN model achieved an AUC score of 0.9845. The best validation loss obtained is 0.1457. The results show that the model can be successfully employed to detect pathological myopia from the fundus images.

Three-dimensional segmentation and depth-encoded visualization of choroidal vasculature using swept-source optical coherence tomography.

The choroid provides nutritional support for the retinal pigment epithelium and photoreceptors. Choroidal dysfunction plays a major role in several of the most important causes of vision loss including age-related macular degeneration, myopic degeneration, and pachychoroid diseases such as central serous chorioretinopathy and polypoidal choroidal vasculopathy. We describe an imaging technique using depth-resolved swept-source optical coherence tomography (SS-OCT) that provides full-thickness three-dimensional (3D) visualization of choroidal anatomy including topographical features of individual vessels. Enrolled subjects with different clinical manifestations within the pachychoroid disease spectrum underwent 15 mm × 9 mm volume scans centered on the fovea. A fully automated method segmented the choroidal vessels using their hyporeflective lumens. Binarized choroidal vessels were rendered in a 3D viewer as a vascular network within a choroidal slab. The network of choroidal vessels was color depth-encoded with a reference to the Bruch's membrane segmentation. Topographical features of the choroidal vasculature were characterized and compared with choroidal imaging obtained with indocyanine green angiography (ICGA) from the same subject. The en face SS-OCT projections of the larger choroid vessels closely resembled to that obtained with ICGA, with the automated SS-OCT approach proving additional depth-encoded 3D information. In 16 eyes with pachychoroid disease, the SS-OCT approach added clinically relevant structural details, including choroidal thickness and vessel depth, which the ICGA studies could not provide. Our technique appears to advance the in vivo visualization of the full-thickness choroid, successfully reveals the topographical features of choroidal vasculature, and shows potential for further quantitative analysis when compared with other choroidal imaging techniques. This improved visualization of choroidal vasculature and its 3D structure should provide an insight into choroid-related disease mechanisms as well as their responses to treatment.

Association Between the Types of Posterior Staphyloma and Their Risk Factors in Pathological Myopia.

Purpose: The purpose of this study was to investigate the relationship of posterior staphyloma (PS) types in pathological myopia (PM) with age, best-corrected visual acuity (BCVA), spherical equivalent (SE), axial length (AL), and myopic retinoschisis (MRS) using three-dimensional magnetic resonance imaging (3D-MRI) and optical coherence tomography (OCT). Methods: This retrospective, cross-sectional study included 108 eyes of 59 patients with PM from the Henan Eye Institute, China. We classified the PS types based on current international classification criteria and analyzed the risk factors (age, AL, MRS, and BCVA). Results: Most of the patients with bilateral PM had the same PS type (80.43%) in both eyes. Complex PS was more common in elderly patients than in younger patients (P < 0.05). Eyes with wide macular PS had a longer AL (P = 0.012) and worse BCVA (P = 0.049) than those with other PS types. MRS was always comorbid with PS (93.33%), and macular PS was the most common (82.14%). Conclusions: Complex PS is associated with patient age. PS is related to MRS, and these changes observed by 3D-MRI and OCT affect the prognosis of visual acuity. The 3D-MRI scans shows changes in the eye shape and PS efficiently. Translational Relevance: In patients with PM, 3D-MRI can guide the planning of posterior scleral reinforcement.

Clinical Characteristics of Highly Myopic Patients With Asymmetric Myopic Atrophic Maculopathy-Analysis Using Multimodal Imaging.

Purpose: To evaluate the factors associated with asymmetric myopic atrophic maculopathy (MAM) in highly myopic patients. Methods: We enrolled highly myopic patients with asymmetric MAM according to the atrophy, traction, and neovascularization (ATN) classification. The results of color fundus photography, optical coherence tomography (OCT), OCT angiography, and corneal visualization Scheimpflug technology (Corvis ST tonometry) were reviewed. The association between inter-eye differences in clinical features and MAM grading was analyzed using logistic regression analysis. Results: Among the 72 eyes of 36 patients 61.0 ± 9.3 years of age, 9, 33, 17, and 13 eyes had A1, A2, A3, and A4, respectively. The mean axial length was 30.44 ± 1.92 mm, and there was no significant difference between eyes with less severe and more severe MAM. The inter-eye differences in MAM grading were associated with the inter-eye differences in the presence of Bruch's membrane defects (P = 0.014), ellipsoid zone disruption (P = 0.013), vessel density of the deep retinal layer (P = 0.022), foveal avascular zone circularity (P = 0.012), foveal avascular zone area (P = 0.049), flow area of the choriocapillaris (P = 0.013), vessel diameter (P = 0.045), and fractal dimension (P = 0.015). No Corvis ST parameter was statistically significant. A higher difference in the choriocapillaris flow area (P = 0.013; adjusted odds ratio = 1.10 [1.02-1.18]) remained associated with higher inter-eye differences in MAM grading in the multivariable regression. Conclusions: A smaller choriocapillaris flow area was associated with more severe MAM, suggesting that vascular factors play pivotal roles in MAM.

Pathological myopia classification with simultaneous lesion segmentation using deep learning.

BACKGROUND AND OBJECTIVES: Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that is characterized by a rising myopia prevalence. We aim to assess the use of convolutional neural networks (CNNs) for the detection of PM and semantic segmentation of myopia-induced lesions from fundus images on a recently introduced reference data set. METHODS: This investigation reports on the results of CNNs developed for the recently introduced Pathological Myopia (PALM) dataset, which consists of 1200 images. Our CNN bundles lesion segmentation and PM classification, as the two tasks are heavily intertwined. Domain knowledge is also inserted through the introduction of a new Optic Nerve Head (ONH)-based prediction enhancement for the segmentation of atrophy and fovea localization. Finally, we are the first to approach fovea localization using segmentation instead of detection or regression models. Evaluation metrics include area under the receiver operating characteristic curve (AUC) for PM detection, Euclidean distance for fovea localization, and Dice and F1 metrics for the semantic segmentation tasks (optic disc, retinal atrophy and retinal detachment). RESULTS: Models trained with 400 available training images achieved an AUC of 0.9867 for PM detection, and a Euclidean distance of 58.27 pixels on the fovea localization task, evaluated on a test set of 400 images. Dice and F1 metrics for semantic segmentation of lesions scored 0.9303 and 0.9869 on optic disc, 0.8001 and 0.9135 on retinal atrophy, and 0.8073 and 0.7059 on retinal detachment, respectively. CONCLUSIONS: We report a successful approach for a simultaneous classification of pathological myopia and segmentation of associated lesions. Our work was acknowledged with an award in the context of the "Pathological Myopia detection from retinal images" challenge held during the IEEE International Symposium on Biomedical Imaging (April 2019). Considering that (pathological) myopia cases are often identified as false positives and negatives in glaucoma deep learning models, we envisage that the current work could aid in future research to discriminate between glaucomatous and highly-myopic eyes, complemented by the localization and segmentation of landmarks such as fovea, optic disc and atrophy.

3-D assessment of gaze-induced eye shape deformations and downgaze-induced vitreous chamber volume increase in highly myopic eyes with staphyloma.

PURPOSE: To determine if the stress of normal eye movements results in gaze-induced globe deformations, vitreous chamber axial length and vitreous chamber axial volume (VCAV) change in highly myopic eyes. METHODS: A prospective imaging study was performed on 82 eyes of 43 patients with high myopia (>27 mm of axial length) with a clinical diagnosis of staphyloma. Three-dimensional MRI scans were acquired while subjects gazed in five directions (primary, nasal, temporal, superior and inferior). Surface renderings were generated, and a processing pipeline was created to automate alignment of the eye and to measure VCAV within 5.5 mm of the visual axis for each eye in every gaze. The degree of gaze-induced globe deformation was determined by calculating the Dice coefficient to assess the degree of overlap of the sclera at each eccentric gaze with that found in primary gaze. Each eccentric gaze VCAV was compared to VCAV in primary gaze using a fixed-effects regression allowing for subject-specific and eye-specific effects. RESULTS: The Dice coefficient showed significant gaze-induced eye shape changes in all gazes (all p<0.0001). There were no statistically significant gaze-induced VCAV changes when comparing primary gaze to nasal, temporal or upgaze. However, when changing from primary to downgaze, VCAV was increased by +4.79 mm3 (p=0.002, 95% CI 1.71 to 7.86). CONCLUSION: Significant gaze-induced globe deformation was noted in all gazes, but a reversible, instantaneous VCAV increase occurred only in downgaze, which is consistent with studies supporting the association of environmental factors such as near work with myopia development and progression.

Importance of Paravascular Vitreal Adhesions for Development of Myopic Macular Retinoschisis Detected by Ultra-Widefield OCT.

PURPOSE: To examine most postequatorial retina in eyes with myopic macular retinoschisis (MRS) by ultra-widefield (UWF) OCT and to determine whether paravascular vitreal adhesions play a role in the development of MRS. DESIGN: Retrospective single-center observational case series. PARTICIPANTS: One hundred fifty highly myopic participants who were older than 50 years with and without an MRS were studied. High myopia was defined as an eye with an axial length of more than 26.5 mm. METHODS: All participants underwent UWF OCT imaging with a scan width of 23 mm and a depth of 5 mm using a prototype swept-source OCT device. The vitreoretinal adhesions to the foveal retina and retinal vessels and paravascular abnormalities, including paravascular retinal cysts, paravascular retinoschisis, and paravascular lamellar holes, were analyzed in the UWF OCT images. The findings in eyes with an MRS were compared with those in eyes without an MRS. MAIN OUTCOME MEASURES: The relationships between MRS and vitreal adhesions to the retinal vessels or to the fovea were determined. RESULTS: An MRS was found in 49 of the 150 eyes (33%). Vitreal adhesions to the retinal vessels were found more frequently in eyes with an MRS than in eyes without an MRS (63% vs. 44%; P = 0.04). In contrast, the number of eyes with adhesions to the fovea in eyes with an MRS was not significantly different from that in eyes without an MRS (57% vs. 59%). Paravascular lesions, for example, retinal cysts, retinoschisis, and lamellar holes, were more common in eyes with an MRS than in eyes without an MRS (71% vs. 36%, 61% vs. 17 %, and 20% vs. 8% [P < 0.001, P < 0.001, and P = 0.03], respectively). Multivariate analysis showed that the presence of paravascular vitreal adhesions was a significant predictor for MRS development (odds ratio, 2.56; P = 0.02). CONCLUSIONS: Paravascular vitreal adhesions may be related to the development of the different types of paravascular lesions including retinal cysts and retinoschisis, and play a more important role in the development of an MRS than vitreal adhesions to the fovea.

QUANTITATIVE TOPOGRAPHIC CURVATURE MAPS OF THE POSTERIOR EYE UTILIZING OPTICAL COHERENCE TOMOGRAPHY.

PURPOSE: Deformations of the retina such as staphylomas in myopia or scleral flattening in high intracranial pressure can be challenging to quantify with en face imaging. We describe an optical coherence tomography-based method for the generation of quantitative posterior eye topography maps in normal and pathologic eyes. METHODS: Using "whole eye" optical coherence tomography, we corrected for subjects' optical distortions to generate spatially accurate posterior eye optical coherence tomography volumes and created local curvature (KM, mm-1) topography maps for each consented subject. We imaged nine subjects, three normal, two with myopic degeneration, and four with papilledema including one that was imaged longitudinally. RESULTS: Normal subjects mean temporal KM was 0.0923 mm-1, nasal KM was 0.0927 mm-1, and KM local variability was 0.0162 mm-1. In myopic degeneration, subjects KM local variability was higher at 0.0836 mm-1. In papilledema subjects nasal KM was flatter compared with temporal KM (0.0709 vs. 0.0885 mm-1). Mean intrasession KM repeatability for all subjects was 0.0036 mm-1. CONCLUSION: We have developed an optical coherence tomography based method for quantitative posterior eye topography that offers the ability to analyze local curvature with micron scale resolution and offers the potential to help clinicians and researchers characterize subtle, local retinal deformations earlier in patients and follow their development over time.

Quantitative analysis with volume rendering of pathological myopic eyes by high-resolution three-dimensional magnetic resonance imaging.

Discovering a relationship between axial length and vitreous volume would be helpful since the axial length is easier to measure than magnetic resonance imaging (MRI) parameters. This study aimed to analyze the topography of human eyes with pathological myopia through volume rendering images by high-resolution 3D-MRI and to establish a model to estimate the vitreous volume.This was a retrospective, non-randomized, controlled study of patients evaluated at Tongren Hospital from July 7, 2007 to December 12, 2018. The controls were emmetropic volunteers. All participants underwent ophthalmic examinations. Axial length was measured with an IOL Master. High-resolution 3D MRI and volume rendering was utilized for all the eyes. Logistic regression was used to establish a model to predict the vitreous volume.A total of 280 emmetropic eyes and 290 eyes with pathological myopia were included. Males represented 60.7% and 65.5% of the individuals. The mean axial lengths of those two groups were 23.1 ±â€Š0.8 mm (95%CI: 22.7-23.4 mm) and 28.3 ±â€Š2.2 mm (95%CI: 27.5-29.2 mm), respectively (P < .001). The regression model in the pathological myopic group for calculating the vitreous volume according to the axial length was: Vitreous volume = 546.27 × axial length - 6977.12. The regression model in the emmetropic group for calculating the vitreous volume according to the axial length was: Vitreous volume = 458.35 × axial length - 6331.14 (R = 0.360, P = .001).Elongation of the axial length is involved in eyeball enlargement in pathological myopic eyes. Measurement of the axial length could be recommended for the estimation of the vitreous volume during vitrectomy if vitreous cavity filling is needed.

Long-term morphologic fundus and optic nerve head pattern of progressive myopia in congenital glaucoma distinguished by age at first surgery.

The purpose of this study was to investigate the preservation of round optic nerve head (ONH) shape in myopic eyes of surgically treated congenital glaucoma patients, with regard to factors associated with intraocular pressure (IOP) elevation-induced peripapillary scleral (PPS) deformation. Using optical coherence tomography (OCT) on the ONH and macula, we identified myopic eyes with round ONH and internally oblique border tissue and those with non-round ONH. We investigated differences in clinical factors between the two groups. We included 51 eyes of 34 patients. Age at first surgery (2.8 vs. 15.2 months, P < 0.001) was significantly different between the two groups. Axial length was also significantly longer (P = 0.004) in the non-round group, but multiple logistic regression analysis revealed age as the only significant factor (P < 0.05) in ONH roundness. Interestingly, the round ONH group also had non-curved fundus morphology and a thick choroid, while the non-round ONH group showed diverse degrees of disc tilt and posterior pole curvature, and a thin choroid. In conclusion, in eyes with congenital glaucoma, age at first surgery, particularly when older than 6 months, was associated with round ONH and emmetropia-like fundus despite high myopia. The findings may indicate two different changes in the posterior sclera and the neural canal in response to the increased IOP.