Background. The management of patients with aphakia and/or lack of capsular support remains debated. The sutureless posterior chamber IOL (PCIOL) fixation is a very useful surgical option. The purpose of the study was to compare the early outcomes as well as post-operative best corrected visual acuity, refractive errors and complications of two different techniques of sutureless PCIOL secondary implantation. Methods. Patients who underwent secondary implantation from December 2019 to January 2021 in the Department of Ophthalmology of Creteil Hospital, and in the Granville Ophthalmology Center, were retrospectively included. Eyes implanted with the iris claw lens (Artisan Aphakia IOL model 205, Ophtec BV, Groningen, The Netherlands) were included in group 1, and eyes implanted with a newly developed sutureless trans-scleral plugs fixated lens (STSPFL, Carlevale lens, Soleko, Pontecorvo, Italy) were included in group 2. Results. Twenty-two eyes of 22 patients were enrolled in group 1, and twenty eyes of 20 patients in group 2. No difference was found in visual acuity between two groups (0.35 +/− 0.29 logmar for group 1 and 0.23 +/− 0.51 logmar for group 2) (p = 0.15) at mean post-operative follow up (6.19 +/− 3.44 months for group 1 and 6.42 +/− 3.96 months for group 2) (p = 0.13). Both the mean refractive error (MRE) and induced astigmatism (IA) were greater in group 1 compared to group 2, respectively: the MRE was 0.99 +/− 0.57 vs. 0.46 +/− 0.36 (p < 0.01), and IA was 1.72 +/− 0.96 vs. 0.72 +/− 0.52 (p < 0.01). Conclusions. No significant differences in terms of the recovery of visual acuity were found between the two groups. Group 2 (STPFL) gives better results in our sample due to less post-operative induced astigmatism and less refractive error.
AimsTo evaluate the long-term progression of quiescent type 1 choroidal neovascularisation (CNV) associated with age-related macular degeneration (AMD) or with pachychoroid disease.MethodsAll cases of quiescent type 1 CNV with a minimum follow-up of 12 months seen at the Department of Ophthalmology of University Paris Est, Creteil and at the Centre Ophtalmologique de l’Odeon, Paris, between June 2009 and December 2018 were retrospectively reviewed. Optical coherence tomography angiography (OCT-A) of eyes not showing CNV activation during 24 months was evaluated for quantitative analyses of CNV status biomarkers (fractal dimension, lacunarity, vessel density, aspect ratio, CNV area).ResultsA total of 67 eyes (65 patients, 43 females, mean age 76.63±9.7 years) with quiescent CNV and a mean follow-up of 49.56±27.3 (12–112) months were included. Of 28 eyes showing activation of quiescent CNV, 12 eyes with pachychoroid-associated CNV showed reduced visual loss (−3.28 ETDRS letters, p=0.7 vs −13.03 ETDRS letters, p=0.02), greater choroidal thinning (−59.5 µm, p=0.03 vs – 16.36 µm, p=0.3) and needed less antivascular endothelial growth factor intravitreal injections (IVI) (0.09 vs 0.21, p=0.01) than 16 eyes with AMD-associated CNV. CNV area was the only OCT-A biomarker to significantly change during 24 months in inactive quiescent CNV (+29.5%, p=0.01, in pachychoroid group and +27.1%, p=0.03, in the AMD group).ConclusionIn the long-term follow-up, inactive quiescent CNV showed an increase of CNV area without significant changes of the other OCT-A biomarkers. Quiescent type 1 CNV undergoing activation showed greater response to IVI when associated to pachychoroid.
Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF). Our purpose was to automatically classify different IRDs by means of FAF images using a deep learning algorithm. Methods. In this study, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD), as well as a healthy comparable group were used to train a multilayer deep convolutional neural network (CNN) to differentiate FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Results. For the inherited retinal disease classifiers, global accuracy was 0.95. The precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD, and 0.989 for healthy controls. Conclusions. This study describes the use of a deep learning-based algorithm to automatically detect and classify inherited retinal disease in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a diagnostic tool and may give relevant information for future therapeutic approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.