To determine the impact of postoperative visual function on the vision-related quality of life (VRQoL) in patients after anatomically successful surgery for macula-off rhegmatogenous retinal ...detachment (RRD) and to propose a classification to grade the extent of macular detachment using preoperative optical coherence tomography (OCT) scans.
This prospective study evaluated 48 patients. At 12 months after surgery, visual function assessments were as follows: metamorphopsia (M-CHARTS), aniseikonia (New Aniseikonia Test), best corrected visual acuity (BCVA) (Early Treatment Diabetic Retinopathy Study ETDRS), low contrast BCVA (10% ETDRS), color vision (Hardy Rand Rittler), and stereopsis (Titmus Fly). VRQoL was assessed by the National Eye Institute Visual Functioning Questionnaire-25 (NEIVFQ-25). Associations between visual function parameters and NEIVFQ-25 scores were evaluated. Preoperative OCT-scans were classified into six stages according to the extent of macular detachment based on an ETDRS-grid: incomplete perifoveal detachment (1), incomplete parafoveal detachment (2), incomplete foveal detachment (3), complete foveal detachment (4), complete parafoveal detachment (5), and complete perifoveal detachment (6).
General vision and driving were the lowest scoring categories. General vision had the strongest correlation with low contrast BCVA (r = -0.41, P = 0.002), while driving had the strongest correlation with stereopsis (r = -0.39, P = 0.008). All macular detachments were graded stage 3 or beyond. Patients with stage 3 macular detachments had the highest visual function values compared to the other stages. The highest percentage of patients with metamorphopsia, aniseikonia and BCVA>0.1 logMAR was found in stages 5 and 6.
Macula-off RRD particularly affects general vision and driving. The extent of macular detachment is a potential predictor for visual function and can be graded using the proposed classification.
To assess if ocular motility impairment, and the ensuing diplopia, after Baerveldt Glaucoma device (BGI) implantation, is related to the presence of a large fluid reservoir (bleb), using Magnetic ...Resonance Imaging (MRI).
In a masked observational study (CCMO-registry number: NL65633.058.18), the eyes of 30 glaucoma patients with (n = 12) or without diplopia (n = 18) who had previously undergone BGI implantation were scanned with a 7 Tesla MRI-scanner. The substructures of the BGI-complex, including both blebs and plate, were segmented in 3D. Primary outcomes were a comparison of volume and height of the BGI-complex between patients with and without diplopia. Comparisons were performed by using an unpaired t-test, Fisher's Exact or Mann-Whitney test. Correlations were determined by using Spearman correlation.
The median volume and height of the BGI-complex was significantly higher in patients with compared to patients without diplopia (p = 0.007 and p = 0.025, respectively). Six patients had an excessively large total bleb volume (median of 1736.5mm3, interquartile range 1486.3-1933.9mm3), four of whom experienced diplopia (33% of the diplopia patients). Fibrotic strands through the BGI plate, intended to limit the height of the bleb, could be visualized but were not related to diplopia (75% versus 88%; p = 0.28).
With MRI, we show that in a significant number of diplopia cases a large bleb is present in the orbit. Given the large volume of these blebs, they are a likely explanation of the development of diplopia in at least some of the patients with diplopia after BGI implantation. Additionally, the MR-images confirm the presence of fibrotic strands. As these strands are also visible in patients with a large bleb, they are apparently not sufficient to restrict the bleb height.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal ...fundus image sets is challenging due to intraand intervisit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of 80% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.
Abstract
Corneal guttae, which are the abnormal growth of extracellular matrix in the corneal endothelium, are observed in specular images as black droplets that occlude the endothelial cells. To ...estimate the corneal parameters (endothelial cell density ECD, coefficient of variation CV, and hexagonality HEX), we propose a new deep learning method that includes a novel attention mechanism (named fNLA), which helps to infer the cell edges in the occluded areas. The approach first derives the cell edges, then infers the well-detected cells, and finally employs a postprocessing method to fix mistakes. This results in a binary segmentation from which the corneal parameters are estimated. We analyzed 1203 images (500 contained guttae) obtained with a Topcon SP-1P microscope. To generate the ground truth, we performed manual segmentation in all images. Several networks were evaluated (UNet, ResUNeXt, DenseUNets, UNet++, etc.) and we found that DenseUNets with fNLA provided the lowest error: a mean absolute error of 23.16 cells/mm
$$^{2}$$
2
in ECD, 1.28 % in CV, and 3.13 % in HEX. Compared with Topcon’s built-in software, our error was 3–6 times smaller. Overall, our approach handled notably well the cells affected by guttae, detecting cell edges partially occluded by small guttae and discarding large areas covered by extensive guttae.
Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment ...retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 μm (0.6-3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.
We present spatial retinal nerve fiber layer (RNFL) attenuation coefficient maps for healthy and glaucomatous eyes based on optical coherence tomography (OCT) measurements. Quantitative analyses of ...differences between healthy and glaucomatous eyes were performed.
Peripapillary volumetric images of 10 healthy and 8 glaucomatous eyes were acquired by a Spectralis OCT system. Per A-line, the attenuation coefficient of the RNFL was determined based on a method that uses the retinal pigment epithelium as a reference layer. The attenuation coefficient describes the attenuation of light in tissue due to scattering and absorption. En-face maps were constructed and visually inspected. Differences between healthy and glaucomatous eyes were analyzed (Mann-Whitney U test), both globally (average values) and spatially (concentric and per segment).
RNFL attenuation coefficient maps of healthy eyes showed relatively high and uniform values. For glaucomatous eyes, the attenuation coefficients were much lower and showed local defects. Normal and glaucomatous average RNFL attenuation coefficients were highly significantly different (P < 0.0001) and fully separable. The RNFL attenuation coefficient decreased with increasing optic nerve head distance for both groups, with highly significant differences for all distances (P < 0.001). The angular dependency showed high superio- and inferiotemporal and low nasal values, with most significant differences superio- and inferiotemporally.
Maps of RNFL attenuation coefficients provide a novel way of assessing the health of the RNFL and are relatively insensitive to imaging artifacts affecting signal intensity. The highly significant difference between normal and glaucomatous eyes suggests using RNFL attenuation coefficient maps as a new clinical tool for diagnosing and monitoring glaucoma.
In conventional phase-resolved OCT blood flow is detected from phase changes between successive A-scans. Especially in high-speed OCT systems this results in a short evaluation time interval. This ...method is therefore often unable to visualize complete vascular networks since low flow velocities cause insufficient phase changes. This problem was solved by comparing B-scans instead of successive A-scans to enlarge the time interval. In this paper a detailed phase-noise analysis of our OCT system is presented in order to calculate the optimal time intervals for visualization of the vasculature of the human retina and choroid. High-resolution images of the vasculature of a healthy volunteer taken with various time intervals are presented to confirm this analysis. The imaging was performed with a backstitched B-scan in which pairs of small repeated B-scans are stitched together to independently control the time interval and the imaged lateral field size. A time interval of ≥ 2.5 ms was found effective to image the retinal vasculature down to the capillary level. The higher flow velocities of the choroid allowed a time interval of 0.64 ms to reveal its dense vasculature. Finally we analyzed depth-resolved histograms of volumetric phase-difference data to assess changes in amount of blood flow with depth. This analysis indicated different flow regimes in the retina and the choroid.
PURPOSE:To explore the attenuation coefficient (AC) of the retinal nerve fiber layer (RNFL) in spectral domain optical coherence tomography (OCT) images, in healthy eyes and eyes affected by ...glaucoma. To assess the relation between RNLF AC, disease severity, RNFL thickness, visual field sensitivity threshold, spatial location and age.
PATIENTS AND METHODS:We analyzed peripapillary circle scans of a clinical OCT device (Spectralis OCT, Heidelberg Engineering, Heidelberg, Germany) in 102 glaucoma patients and 90 healthy controls. The images were fully automatically converted into depth-resolved AC images. Next, the median AC within the RNFL was calculated based on the Spectralis segmentation. We compared the RNFL AC between healthy, mild, moderate and advanced glaucomatous eyes and assessed the correlation with patient characteristics such as age and visual field sensitivity threshold (HFA, Carl Zeiss Meditec, Dublin, USA) in a generalized estimating equations (GEE) model. Finally, we explored the ability to discriminate between glaucomatous and healthy eyes by RNFL AC.
RESULTS:Median RNFL AC decreased with increasing disease severity up to moderate glaucoma (P<0.001) in all four sectors around the optic nerve head. The largest relative decrease occurred in the nasal sector. The RNFL AC (AUC 0.834±0.028) effectively discriminated healthy from glaucomatous eyes, although RNFL thickness (AUC 0.975±0.013) performed even better (P<0.001). Prediction of visual field sensitivity improved significantly when RNFL thickness was augmented with RNFL AC as covariates (P<0.001).
CONCLUSIONS:This study demonstrated that RNFL AC provides complementary information on the RNFL’s health compared to RNFL thickness measurements alone.
To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the ...performance of a large cohort of ophthalmologists and optometrists.
A retrospective study evaluating the diagnostic performance of an AI software (Pegasus v1.0, Visulytix Ltd., London UK) and comparing it with that of 243 European ophthalmologists and 208 British optometrists, as determined in previous studies, for the detection of glaucomatous optic neuropathy from 94 scanned stereoscopic photographic slides scanned into digital format.
Pegasus was able to detect glaucomatous optic neuropathy with an accuracy of 83.4% (95% CI: 77.5-89.2). This is comparable to an average ophthalmologist accuracy of 80.5% (95% CI: 67.2-93.8) and average optometrist accuracy of 80% (95% CI: 67-88) on the same images. In addition, the AI system had an intra-observer agreement (Cohen's Kappa, κ) of 0.74 (95% CI: 0.63-0.85), compared with 0.70 (range: -0.13-1.00; 95% CI: 0.67-0.73) and 0.71 (range: 0.08-1.00) for ophthalmologists and optometrists, respectively. There was no statistically significant difference between the performance of the deep learning system and ophthalmologists or optometrists.
The AI system obtained a diagnostic performance and repeatability comparable to that of the ophthalmologists and optometrists. We conclude that deep learning based AI systems, such as Pegasus, demonstrate significant promise in the assisted detection of glaucomatous optic neuropathy.
Traditionally, preoperative posturing consisting of bed rest and positioning is prescribed to patients with macula-on retinal detachment (RD) to prevent RD progression and detachment of the fovea. ...Execution of such advice can be cumbersome and expensive. This study aimed to investigate if preoperative posturing affects the progression of RD.
Prospective cohort study.
Ninety-eight patients with macula-on RD were included. Inclusion criteria were volume optical coherence tomography (OCT) scans could be obtained with sufficient quality; and the smallest distance from the fovea to the detachment border was 1.25 mm or more.
Patients were admitted to the ward for bed rest in anticipation of surgery and were positioned on the side where the RD was mainly located. At baseline and before and after each interruption for meals or toilet visits, a 37°×45° OCT volume scan was performed using a wide-angle Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany). The distance between the nearest point of the RD border and fovea was measured using a custom-built measuring tool.
The RD border displacement and the average RD border displacement velocity moving toward (negative) or away (positive) from the fovea were determined for intervals of posturing and interruptions.
The median duration of intervals of posturing was 3.0 hours (interquartile range IQR, 1.8–14.0 hours; n = 202) and of interruptions 0.37 hours (IQR, 0.26–0.50 hours; n = 197). The median RD border displacement was 2 μm (IQR, −65 to +251 μm) during posturing and −61 μm (IQR, −140 to 0 μm) during interruptions, a statistically significant difference (P < 0.001, Mann–Whitney U test). The median RD border displacement velocity was +1 μm/hour (IQR, −21 to +49 μm/hour) during posturing and −149 μm/hour (IQR, −406 to +1 μm/hour) during interruptions, a statistically significant difference (P < 0.001).
By making use of usual interruptions of preoperative posturing we were able to show, in a prospective and ethically acceptable manner, that RD stabilizes during posturing and progresses during interruptions in patients with macula-on RD. Preoperative posturing is effective in reducing progression of RD.
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