Long-Term Results after DMEK (Descemet’s Membrane Endothelial Keratoplasty)

Ziel der Arbeit: Evaluation der langfristigen Ergebnisse sowie der Komplikationsrate nach Descemet’s Membran Endothelialen Keratoplastik (DMEK) Methoden: Eine cross-sectional, Fall-Serien Studie. Insgesamt wurden 230 Augen von 142 Patienten, die zwischen 2010 und 2014 eine DMEK an der Universitäts-Augenklinik Marburg bekommen haben, untersucht. Die best-korrigierte Sehschärfe (BCVA), die Refraktion, die zentrale Hornhautdicke, das Hornhautvolumen sowie die Endothelialzelldichte wurden als Parameter herangezogen und mit den präoperativen Befunden verglichen. Die Transplantat-Überlebensrate sowie die postoperativen Komplikationen wurden ebenfalls betrachtet. Ergebnisse: Die Nachbeobachtungszeit betrug 47 ± 13.3 Monate. Bei den Patienten die keine anderen okuläre Erkrankungen hatten hat sich die BCVA von 0.60 ± 0.32 logMAR präoperativ auf bis zu 0.10 ± 0.22 logMAR verbessert (201 Augen). 71.1% dieser Patienten hatten eine BCVA von 0.11 logMAR oder besser (≥ 0.8 dezimal), wobei 49.2% dieser Patienten eine volle BCVA von 0.00 logMAR oder besser erreicht haben. Die zentrale Hornhautdicke hat von 675 ± 112µm präoperativ auf 547 ± 52 µm in der letzten Follow-up Untersuchung abgenommen, und das Hornhautvolumen hat von 65.2 ± 8.4 mm2 präoperativ auf 61.9 ± 5.4 mm2 abgenommen. Der Endothelzellverlust lag bei 1392 ± 455 Zellen/mm², was einem durchschnittlichen Verlust von 54.7% der Transplantatzellen entspricht. Die Transplantat-Überlebensrate lag bei 92% mit einer durchschnittlichen Überlebenszeit von 76.6 ± 1.3 Monaten. Schlussfolgerung: DMEK bietet hohe visuelle Ergebnisse und sehr gute klinische Befunde, die mehrere Jahre nach der Operation stabil bleiben können. Durch die hohe Transplantat-Überlebensrate und die niedrige postoperative Komplikationsrate wird DMEK derzeit als erste Wahl zur Behandlung von Endothelzellerkrankungen eingesetzt

The variability of corneal and anterior segment parameters in keratoconus

Purpose: To analyse, describe and test diverse corneal and anterior segment parameters in normal and keratoconic eyes to better understand the geometry of the keratoconic cornea. Method: 44 eyes from 44 keratoconic patients and 44 eyes from 44 healthy patients were included in the study. The Pentacam System was used for the analysis of the anterior segment parameters. New ad-hoc parameters were defined by measuring the distances on the Scheimpflug image at the horizontal diameter, with chamber depth now comprising of two distinctive distances: corneal sagittal depth and the distance from the endpoint of this segment to the anterior surface of the lens (DL). Results: Statistically significant differences (p<0.05) between normal and keratoconic eyes were found in all of the analysed corneal parameters. Anterior chamber depth presented statistical differences between normal and keratoconic eyes (3.06 ± 0.43 mm versus 3.34 ± 0.45 mm, respectively; p = 0.004). This difference was found to originate in an increase of the DL distance (0.40 ± 0.33 mm in normal eyes against 0.61 ± 0.45 mm in keratoconic eyes; p = 0.014), rather than in the changes in corneal sagittal depth. Conclusion: These findings indicate that keratoconus results in central and peripheral corneal manifestations, as well as changes in the shape of the scleral limbus. The DL parameter was useful in describing the forward elongation and advance of the scleral tissue in keratoconic eyes. This finding may help in the monitoring of disease progression and contact lens design and fitting.Preprin

Contact lens classification by using segmented lens boundary features

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods

Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images

Glaucoma, the silent thief of vision, is mostly caused by the gradual increase of pressure in the eye which is known as intraocular pressure (IOP). An effective way to prevent the rise in eye pressure is by early detection. Prior computer vision-based work regarding IOP relies on fundus images of the optic nerves. This paper provides a novel vision-based framework to help in the initial IOP screening using only frontal eye images. The framework first introduces the utilization of a fully convolutional neural (FCN) network on frontal eye images for sclera and iris segmentation. Using these extracted areas, six features that include mean redness level of the sclera, red area percentage, Pupil/Iris diameter ratio, and three sclera contour features (distance, area, and angle) are computed. A database of images from the Princess Basma Hospital is used in this work, containing 400 facial images; 200 cases with normal IOP; and 200 cases with high IOP. Once the features are extracted, two classifiers (support vector machine and decision tree) are applied to obtain the status of the patients in terms of IOP (normal or high). The overall accuracy of the proposed framework is over 97.75% using the decision tree. The novelties and contributions of this work include introducing a fully convolutional network architecture for eye sclera segmentation, in addition to scientifically correlating the frontal eye view (image) with IOP by introducing new sclera contour features that have not been previously introduced in the literature from frontal eye images for IOP status determination.https://doi.org/10.1109/JTEHM.2019.291553

Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via Anterior Segment Optical Coherence Tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch. In our method, a sliding window based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel sub-networks are applied to extract AS-OCT representations for the global image and at clinically-relevant local regions. Finally, the extracted deep features of these sub-networks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.Comment: 9 pages, accepted by IEEE Transactions on Cybernetic