Dry Eye Disease in the Middle East and Northern Africa: A Position Paper on the Current State and Unmet Needs.

In the Middle East and Northern Africa (MENA), dry eye disease (DED) is often misdiagnosed or overlooked. This review summarizes a series of conversations with ophthalmologists in the region around a variety of climatic, lifestyle, and iatrogenic factors that contribute to specific features of DED in the MENA region. These considerations are further classified by patient lifestyle and surgical choices. All statements are based on discussions and formal voting to achieve consensus over three meetings. Overall, a deeper understanding of the disease characteristics of DED specific to MENA can better guide local eyecare practitioners on appropriate management and follow-up care. Additionally, population-based studies and patient and physician education on ocular surface diseases, together with the use of culturally appropriate and language-specific questionnaires can help ease the public health burden of DED in this region.

Enhancing the Automated Detection of Implantable Collamer Lens Vault Using Generative Adversarial Networks and Synthetic Data on Optical Coherence Tomography.

PURPOSE: To investigate the efficacy of incorporating Generative Adversarial Network (GAN) and synthetic images in enhancing the performance of a convolutional neural network (CNN) for automated estimation of Implantable Collamer Lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). METHODS: This study was a retrospective evaluation using synthetic data and real patient images in a deep learning framework. Synthetic ICL AS-OCT scans were generated using GANs and a secondary image editing algorithm, creating approximately 100,000 synthetic images. These were used alongside real patient scans to train a CNN for estimating ICL vault distance. The model's performance was evaluated using statistical metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) for the estimation of ICL vault distance. RESULTS: The study analyzed 4,557 AS-OCT B-scans from 138 eyes of 103 patients for training. An independent, retrospectively collected dataset of 2,454 AS-OCT images from 88 eyes of 56 patients, used prospectively for evaluation, served as the test set. When trained solely on real images, the CNN achieved a MAPE of 15.31%, MAE of 44.68 µm, and RMSE of 63.3 µm. However, with the inclusion of GAN-generated and algorithmically edited synthetic images, the performance significantly improved, achieving a MAPE of 8.09%, MAE of 24.83 µm, and RMSE of 32.26 µm. The R2 value was +0.98, indicating a strong positive correlation between actual and predicted ICL vault distances (P < .01). No statistically significant difference was observed between measured and predicted vault values (P = .58). CONCLUSIONS: The integration of GAN-generated and edited synthetic images substantially enhanced ICL vault estimation, demonstrating the efficacy of GANs and synthetic data in enhancing OCT image analysis accuracy. This model not only shows potential for assisting postoperative ICL evaluations, but also for improving OCT automation when data paucity is an issue. [J Refract Surg. 2024;40(4):e199-e207.].

Creating realistic anterior segment optical coherence tomography images using generative adversarial networks.

AIMS: To develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images. METHODS: This study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images. To assess the suitability of the generated images for machine learning tasks, a convolutional neural network (CNN) was trained using a dataset of real and generated images over a classification task. The generated AS-OCT images were then upsampled using an enhanced super-resolution GAN (ESRGAN) to achieve high resolution. RESULTS: The generated images exhibited visual and quantitative similarity to real AS-OCT images. Quantitative similarity assessed using FID scored an average of 6.32. Surgeons scored 51.7% in identifying real versus generated images which was not significantly better than chance (p value >0.3). The CNN accuracy improved from 78% to 100% when synthetic images were added to the dataset. The ESRGAN upsampled images were objectively more realistic and accurate compared with traditional upsampling techniques by scoring a lower Learned Perceptual Image Patch Similarity of 0.0905 compared with 0.4244 of bicubic interpolation. CONCLUSIONS: This study successfully developed and leveraged GANs capable of generating high-definition synthetic AS-OCT images that are realistic and suitable for machine learning and image analysis tasks.

Repeatability of pyramidal aberrometer measurements in keratoconus and normal eyes.

PURPOSE: To evaluate the intrasubject repeatability of pyramidal aberrometer measurements in a sample of keratoconus and normal eyes. SETTING: American University of Beirut Medical Center, Beirut, Lebanon. DESIGN: Prospective comparative repeatability analysis. METHODS: Study population: Keratoconus and normal eyes from adult patients. Observation procedures: Each eye was evaluated with 3 consecutive acquisitions using a pyramidal aberrometer. Main outcome measures: The repeatability of different ocular higher-order aberrations and lower-order aberrations (HOAs and LOAs, respectively), and Zernike coefficients down to the fifth order, was evaluated. Repeatability was assessed by within-subject SDs (Sw), repeatability limits ( r ), and intraclass correlation coefficients (ICCs), among other parameters. RESULTS: 72 keratoconus patients (72 eyes) and 76 normal patients (76 eyes) were included. In normal and keratoconus eyes, the ICC of total LOAs and HOAs, as well as each of the Zernike coefficients, was >0.9. The Sw for keratoconus eyes with mean maximal keratometry (Kmax) <50 diopters (D) was 0.1345 for total LOAs, 0.0619 for total HOAs, 0.0292 for horizontal coma, 0.0561 for vertical coma, and 0.0221 for spherical aberration as compared with 0.2696, 0.1486, 0.0972, 0.1497, and 0.0757 for keratoconus eyes with Kmax ≥50 D. Similar trend of better repeatability for grade 1 keratoconus and HOAs <2 D as compared with grades 2 and 3 keratoconus and eyes with HOAs >2 D were also noted. CONCLUSIONS: Ocular aberrometer measurements generated by high definition pyramidal aberrometers have high repeatability in both normal and mild keratoconus eyes and moderate repeatability, yet still clinically acceptable, in advanced keratoconus. This is of particular importance in ocular wavefront-guided treatments.