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PURPOSE: The aim is to analyze automated quantification of choriocapillaris flow deficit (CCFD) on swept-source (SS)-optical coherence tomography angiography (OCTA) in tubercular serpiginous-like choroiditis (TBSLC).METHODS: In this prospective study, automated CCFD calculations were performed on SS-OCTA and compared with CCFD areas on indocyanine green angiography (ICGA). Patients were divided into two groups based on the occurrence of paradoxical worsening (PW).RESULTS: Twenty-nine eyes (29 subjects; 18 males; mean age: 33±12 years) were included. The mean CCFD at baseline was 34.9 ± 4.3% on OCTA in eyes without PW and 35.4 ± 5.0% on SS-OCTA with PW (p = .77). At 4 and 12 weeks, CCFD on SS-OCTA improved to 30.6 ± 3.9% and 28.0 ± 4.2% (p < .001) without PW, respectively, and increased to 42.9 ± 4.4% and 48.8 ± 4.1% (p < .001) with PW, respectively. The SS-OCTA CCFD correlated well with ICGA (r = 0.48; p < .001).CONCLUSIONS: Automated quantitative serial assessment of CCFD on SS-OCTA can serve as a useful biomarker of disease activity in eyes with TBSLC.

In this research, we studied the duality between cataractous retinal image dehazing and image denoising and proposed that the dehazing task for cataractous retinal images can be achieved with the combination of image denoising and sigmoid function. To do so, we introduce the double-pass fundus reflection model in the YPbPr color space and developed a multilevel stimulated denoising strategy termed MUTE. The transmission matrix of the cataract layer is expressed as the superposition of denoised raw images of different levels weighted by pixel-wise sigmoid functions. We further designed an intensity-based cost function that can guide the updating of the model parameters. They are updated by gradient descent with adaptive momentum estimation, which gives us the final refined transmission matrix of the cataract layer. We tested our methods on cataract retinal images from both public and proprietary databases, and we compared the performance of our method with other state-of-the-art enhancement methods. Both visual assessments and objective assessments show the superiority of the proposed method. We further demonstrated three potential applications including blood vessel segmentation, retinal image registrations, and diagnosing with enhanced images that may largely benefit from our proposed methods.

Primary open-angle glaucoma (POAG) is characterized by optic nerve degeneration and irreversible loss of retinal ganglion cells (RGCs). The pathophysiology is not fully understood. Since RGCs have a high energy demand, suboptimal mitochondrial function may put the survival of these neurons at risk. In the present study, we explored whether mtDNA copy number or mtDNA deletions could reveal a mitochondrial component in POAG pathophysiology. Buffy coat DNA was isolated from EDTA blood of age- and sex-matched study groups, namely POAG patients with high intraocular pressure (IOP) at diagnosis (high tension glaucoma: HTG; n = 97), normal tension glaucoma patients (NTG, n = 37), ocular hypertensive controls (n = 9), and cataract controls (without glaucoma; n = 32), all without remarkable comorbidities. The number of mtDNA copies was assessed through qPCR quantification of the mitochondrial D-loop and nuclear B2M gene. Presence of the common 4977 base pair mtDNA deletion was assessed by a highly sensitive breakpoint PCR. Analysis showed that HTG patients had a lower number of mtDNA copies per nuclear DNA than NTG patients (p-value

Background Keratoconus is a degenerative disorder of the cornea leading to a protrusion and thinning with loss of visual acuity. The only treatment to halt the progression is corneal crosslinking (CXL), which uses riboflavin and UV-A light to stiffen the cornea. Recent ultra-structural examinations show that the disease is regional and does not affect the entire cornea. Treating only the affected zone with CXL could be as good as the standard CXL, that treats the entire cornea. Methods We set up a multicentre non-inferiority randomized controlled clinical trial comparing standard CXL (sCXL) and customized CXL (cCXL). Patients between 16 and 45 years old with progressive keratoconus were included. Progression is based on one or more of the following changes within 12 months: 1 dioptre (D) increase in keratometry (Kmax, K1, K2); or 10% decrease of corneal thickness; or 1 D increase in myopia or refractive astigmatism, requiring corneal crosslinking. Discussion The goal of this study is to evaluate whether the effectiveness of cCXL is non-inferior to sCXL in terms of flattening of the cornea and halting keratoconus progression. Treating only the affected zone could be beneficial for minimalizing the risk of damaging surrounding tissues and faster wound healing. Recent non-randomized studies suggest that a customized crosslinking protocol based on the tomography of the patient’s cornea may stop the progression of keratoconus and result in flattening of the cornea. Trial registration This study was prospectively registered at ClinicalTrials.gov on August 31st, 2020, the identifier of the study is NCT04532788.

Contemporary psychiatric diagnosis still relies on the subjective symptom report of the patient during a clinical interview by a psychiatrist. Given the significant variability in personal reporting and differences in the skill set of psychiatrists, it is desirable to have objective diagnostic markers that could help clinicians differentiate patients from healthy individuals. A few recent studies have reported retinal vascular abnormalities in patients with schizophrenia (SCZ) using retinal fundus images. The goal of this study was to use a trained convolution neural network (CNN) deep learning algorithm to detect SCZ using retinal fundus images. A total of 327 subjects [139 patients with Schizophrenia (SCZ) and 188 Healthy volunteers (HV)] were recruited, and retinal images were acquired using a fundus camera. The images were preprocessed and fed to a convolution neural network for the classification. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The CNN achieved an accuracy of 95% for classifying SCZ and HV with an AUC of 0.98. Findings from the current study suggest the potential utility of deep learning to classify patients with SCZ and assist clinicians in clinical settings. Future studies need to examine the utility of the deep learning model with retinal vascular images as biomarkers in schizophrenia with larger sample sizes.

BACKGROUND AND OBJECTIVE: The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. The background mismatch reduces the experimental robustness of qDPC and largely limits its applications. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC.METHODS: In Retinex-qDPC, we replace the data fidelity term of the previous cost function for qDPC inverse problem, by the images' edge features yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L1-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data.RESULTS: Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art qDPC algorithms.CONCLUSIONS: The Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications.

We present a simple but efficient and robust reconstruction algorithm for Fourier ptychographic microscopy, termed error-laxity Fourier ptychographic iterative engine (Elfpie), that is simultaneously robust to (1) noise signal (including Gaussian, Poisson, and salt & pepper noises), (2) problematic background illumination problem, (3) vignetting effects, and (4) misaligning of LED positions, without the need of calibrating or recovering of these system errors. In Elfpie, we embedded the inverse problem of FPM under the framework of feature extraction/recovering and proposed a new data fidelity cost function regularized by the global second-order total-variation regularization (Hessian regularization). The closed-form complex gradient for the cost function is derived and is back-propagated using the AdaBelief optimizer with an adaptive learning rate to update the entire Fourier spectrum of the sample and system pupil function. The Elfpie is tested on both simulation data and experimental data and is compared against the state-of-the-art (SOTA) algorithm. Results show the superiority of the Elfpie among other SOTA methods, in both reconstruction quality under different degeneration issues, and implementation efficiency. In general, compared against SOTA methods, the Elfpie is robust to Gaussian noise with 100 times larger noise strength, salt & pepper noise with 1000 times larger noise strength, and Poisson noise with 10 times noise strength. The Elfpie is able to reconstruct high-fidelity sample field under large LED position misalignments up to 2 mm. It can also bypass the vignetting effect in which all SOTA methods fail to reconstruct the sample pattern. With parallel computation, the Elfpie is able to be K times faster than traditional FPM, where K is the number of used LEDs.

PURPOSE: The purpose of this systematic review is to identify techniques used for quantification of choriocapillaris (CC) flow in eyes with uveitis using optical coherence tomography angiography (OCTA), report reliability and level of correlation with techniques such as indocyanine green angiography (ICGA). METHODS: A systematic search of several databases was done. The studies were analyzed for techniques of measurement, reliability, and correlation with other modalities. Risk of bias assessment was performed. RESULTS: Thirteen studies were included. CC vessel density (7 studies) and flow deficit area (4 studies) were the most used indices. There was significant heterogeneity in the studies due to differences in the scan protocol, thresholding strategy, and analysis. Comparison with ICGA was performed by only one study, and reliability indices were reported by only two studies. CONCLUSION: OCTA is a useful tool to measure the CC vascularity in eyes with uveitis. However, standardized acquisition and analysis protocols are needed.

Raman aqueous humor detection provides a non-invasive, molecular-level approach for ingredient analysis within aqueous humor. However, current Raman aqueous humor applications are facing low signal-to-noise levels due to the trade-off between laser power and laser safety. In order to increase Raman signal while guaranteeing laser safety, in the research, we propose to use the abruptly auto-defocusing (AADF) beam as the illumination source for Raman aqueous humor spectroscopy. The ray-tracing sketch together with the propagation simulation of AADF shows its evolution within the aqueous humor. The intensity distributions are analyzed with and without the impact of corneal refraction. Results show that the efficiency of AADF is higher than the conventional focused Gaussian beam (FGB) method with center blocked. The peak value of the Raman signal intensity acquired using the AADF method is about 6 times larger than that of an FGB.