IntroductionThis is an interventional prospective clinical study which was conducted to evaluate the efficacy, safety, predictability, ocular aberrations, and flap thickness predictability of Visumax femtosecond laser (FSL) compared to Moria M2 microkeratome (MK) in mild to moderate myopia.MethodsThis study included 60 eyes who were divided into two groups. Thirty eyes in group (I) in which the flap was created with Visumax FSL, while in group II (30 eyes) the Moria M2 MK was used. Keratometric, refractive, and aberrometric measurements were compared preoperatively and 3 months postoperatively. The intraoperative subtraction pachymetry (the SP 100 Handy pachymeter (Tomey, Nagoya, Japan) was used for preoperative pachymetry and flap thickness measurement.ResultsNo significant difference was found between the two groups in regards to postoperative manifest sphere, spherical equivalent, astigmatism, safety indices nor ocular aberrations. Twenty six eyes (86.6%) in group I and 23 eyes in group II (76.6%) were within ±0.5D of the intended correction and 23 eyes (76.6%) in group I and 15 eyes in group II (50%) were within ±0.25D of the intended correction. In group I, the mean postoperative actual flap thickness was 100.12 ± 16.1 μm (81 to 122 μm), while in group II, it was 104.6 ± 20.1 μm (62 to 155 μm). The difference was statistically significant (p = 0.001).ConclusionsBoth Visumax and Moria M2 MK are safe and effective in treating myopia with no statistically significant difference in induction of ocular aberrations but with potential advantage for Visumax regarding predictability. More accurate flap thickness is achieved with Visumax femtolasik.Trial registrationThis study was retrospectively registered on 19/6/2017. Trial registration number NCT03193411, clinicalTrials.gov.

Aim. To compare objective and subjective outcome after simultaneous wave front guided (WFG) PRK and accelerated corneal cross-linking (CXL) in patients with progressive keratoconus versus sequential WFG PRK 6 months after CXL. Methods. 62 eyes with progressive keratoconus were divided into two groups; the first including 30 eyes underwent simultaneous WFG PRK with accelerated CXL. The second including 32 eyes underwent subsequent WFG PRK performed 6 months later after accelerated CXL. Visual, refractive, topographic, and aberrometric data were determined preoperatively and during 1-year follow-up period and the results compared in between the 2 studied groups. Results. All evaluated visual, refractive, and aberrometric parameters demonstrated highly significant improvement in both studied groups (all P < 0.001). A significant improvement was observed in keratometric and Q values. The improvement in all parameters was stable till the end of follow-up. Likewise, no significant difference was determined in between the 2 groups in any of recorded parameters. Subjective data revealed similarly significant improvement in both groups. Conclusions. WFG PRK and accelerated CXL is an effective and safe option to improve the vision in mild to moderate keratoconus. In one-year follow-up, there is no statistically significant difference between the simultaneous and sequential procedure.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.

Diabetic retinopathy (DR) is a major public health problem and the leading cause of vision loss in the working age population. This paper presents a novel deep learning system for the detection and diagnosis of DR using optical coherence tomography (OCT) images. The input for this system is three-channel local and global information from OCT images. The local high-level information is represented by the thickness channel and the reflectivity channel. The global low-level information is represented by the grey-level OCT original image. The deep learning system processes the three-channel input to produce the final DR diagnoses. Experimental results on 200 OCT images, augmented to 800 images, which are collected by the University of Louisville, show high system performance related to other competing methods. Moreover, 10-fold and leave-one-subject-out (LOSO) experiments are performed to confirm how significant using the fused images is in improving the performance of the diagnoses, by investigating four different CNN architectures . All of the four architectures achieve acceptable performance and confirm a significant performance improvement using the fused images. Using LOSO, the best network performance has improved from 90.1 ± 2% using only the grey level dataset to 97.7 ± 0.5% using the proposed fused dataset. These results confirm the promise of using the proposed system for the detection of DR using OCT images.

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