High-throughput screening of cytochrome P450 (CYP) family of genes in primary and recurrent pterygium.

Pterygium is a common degenerative disease characterized by fibrovascular outgrowth towards cornea. Around 200 million people have been reported to be affected by the pterygium in the world. Although the risk factors for pterygium are well documented, the molecular pathogenesis of pterygium seems to be very complex and remains highly elusive. However, the common sense for the development of pterygium appears to be deregulation of growth hemostasis due to aberrant apoptosis. In addition, pterygium shares many features with human cancers, including dysregulation of apoptosis, persistent proliferation, inflammation, invasion, and relapse following resection. Cytochrome P450 (CYP) monooxygenases are a superfamily of heme-containing enzymes with a wide range of structural and functional diversity. In the present study, we aimed to identify significant expression signatures of CYP gene in pterygium. For the study, a total number of 45 patients (30 primary and 15 recurrent pterygium) were included. For the high-throughput screening of CYP gene expression, Fluidigm 96.96 Dynamic Array Expression Chip was used and analyzed with BioMark™ HD System Real-Time PCR system. Remarkably, CYP genes were identified to be significantly overexpressed in both primary and recurrent pterygium samples. Most prominent overexpression was observed in CYP1A1, CYP11B2 and CYP4F2 in primary pterygium and CYP11A1 and CYP11B2 in recurrent pterygium. Consequently, present findings suggest the significant involvement of CYP genes in the development and progression of pterygium.

Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease.

OBJECTIVE: To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests. METHODS: In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI). RESULTS: Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT. CONCLUSION: Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.

Prediction of corneal graft rejection using central endothelium/Descemet's membrane complex thickness in high-risk corneal transplants.

To determine whether measurements of Endothelium/Descemet complex thickness (En/DMT) are of predictive value for corneal graft rejection after high-risk corneal transplantation, we conducted this prospective, single-center, observational case series including sixty eyes (60 patients) at high risk for corneal graft rejection (GR) because of previous immunologic graft failure or having at least two quadrants of stromal vascularization. Patients underwent corneal transplant. At 1st, 3rd, 6th, 9th, and 12th postoperative month, HD-OCT imaging of the cornea was performed, and the corneal status was determined clinically at each visit by a masked cornea specialist. Custom-built segmentation tomography algorithm was used to measure the central En/DMT. Relationships between baseline factors and En/DMT were explored. Time dependent covariate Cox survival regression was used to assess the effect of post-operative En/DMT changes during follow up. A longitudinal repeated measures model was used to assess the relationship between En/DMT and graft status. Outcome measures included graft rejection, central Endothelium/Descemet's complex thickness, and central corneal thickness (CCT). In patients with GR (35%), the central En/DMT increased significantly 5.3 months (95% CI: 2, 11) prior to the clinical diagnosis of GR, while it remained stable in patients without GR. During the 1-year follow up, the rejected grafts have higher mean pre-rejection En/DMTs (p = 0.01), compared to CCTs (p = 0.7). For En/DMT ≥ 18 µm cut-off (at any pre-rejection visit), the Cox proportional hazard ratio was 6.89 (95% CI: 2.03, 23.4; p = 0.002), and it increased to 9.91 (95% CI: 3.32, 29.6; p < 0.001) with a ≥ 19 µm cut-off. In high-risk corneal transplants, the increase in En/DMT allowed predicting rejection prior to the clinical diagnosis.

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images. STUDY DESIGN: Development of a deep learning neural network diagnosis algorithm. METHODS: A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1 score and 95% confidence intervals (CIs) were computed. RESULTS: The MDDN achieved eye-level AUROCs >0.99 (95% CI: 0.90, 1.0), AUPRCs > 0.96 (95% CI: 0.90, 1.0), and F1 scores > 0.90 (95% CI: 0.81, 1.0) for DES, FED, and KCN, respectively. CONCLUSIONS: MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, and DES using only AS-OCT images.

Case Series of Perforated Keratomycosis after Laser-Assisted In Situ Keratomileusis.

BACKGROUND: Fungal keratitis is an extremely rare complication of laser vision correction resulting in poor visual outcomes. Amniotic membrane transplantation should be kept in mind in eyes with corneal perforation prior to penetrating keratoplasty. AIM: To assess the outcomes of multilayered fresh amniotic membrane transplantation (MLF-AMT) in patients with severe keratomycosis after laser-assisted in situ keratomileusis (LASIK). Study design. Hospital-based prospective interventional case series. METHODS: Five eyes of 5 patients were included in the study. All cases underwent microbiological scrapings from residual bed and intrastromal injections of amphotericin (50 mcg/mL), with flap amputation if needed, followed by topical 5% natamycin and 0.15% amphotericin. MLF-AMT was performed after corneal perforation. Later, penetrating keratoplasty (PK) was performed when corneal opacity compromised visual acuity. The outcome measures were complete resolution of infection, corneal graft survival, and best-corrected visual acuity (BCVA). RESULTS: The mean age of patients was 22 ± 1.2 years with 4/5 (80%) were females. The mean interval between LASIK and symptom onset was 8.8 ± 1 day, and the mean interval between symptom onset and referral was 14 ± 1.4 days. Potassium hydroxide (KOH) smears showed filamentous fungi, and Sabouraud's medium grew Aspergillus in all cases. Melted flaps were amputated in 4 (80%) cases. MLF-AMT was performed in all cases due to corneal perforation after a mean time of 12.4 ± 1.2 days of antifungals. In all cases, complete resolution of infection was seen 26 ± 1.8 days after MLF-AMT, and optical PK was done at a mean of 2.4 months later. No postoperative complications after MLF-AMT or PK were observed, with a 0% incidence of corneal graft rejection, and a final BCVA ranged from 20/20 to 20/80 after a mean follow-up of 14 ± 1.1 months. CONCLUSION: MLF-AMT is a safe and valid option to manage corneal perforation during keratmycosis treatment to avoid emergency therapeutic keratoplasty.

Mobility improvement of patients with peripheral visual field losses using novel see-through digital spectacles.

PURPOSE: To evaluate see-through Augmented Reality Digital spectacles (AR DSpecs) for improving the mobility of patients with peripheral visual field (VF) losses when tested on a walking track. DESIGN: Prospective Case Series. PARTICIPANTS: 21 patients with peripheral VF defects in both eyes, with the physical ability to walk without assistance. METHODS: We developed the AR DSpecs as a wearable VF aid with an augmented reality platform. Image remapping algorithms produced personalized visual augmentation in real time based on the measured binocular VF with the AR DSpecs calibration mode. We tested the device on a walking track to determine if patients could more accurately identify peripheral objects. MAIN OUTCOME MEASURES: We analyzed walking track scores (number of recognized/avoided objects) and eye tracking data (six gaze parameters) to measure changes in the kinematic and eye scanning behaviors while walking, and assessed a possible placebo effect by deactivating the AR DSpecs remapping algorithms in random trials. RESULTS: Performance, judged by the object detection scores, improved with the AR DSpecs (P<0.001, Wilcoxon rank sum test) with an average improvement rate of 18.81%. Two gaze parameters improved with the activated algorithm (P<0.01, paired t-test), indicating a more directed gaze on the central path with less eye scanning. Determination of the binocular integrated VF with the DSpecs correlated with the integrated standard automated perimetry (R = 0.86, P<0.001), mean sensitivity difference 0.8 ± 2.25 dB (Bland-Altman). CONCLUSIONS: AR DSpecs may improve walking maneuverability of patients with peripheral VF defects by enhancing detection of objects in a testing environment.

Automated diagnosis and staging of Fuchs' endothelial cell corneal dystrophy using deep learning.

BACKGROUND: To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs' endothelial corneal dystrophy (FECD) without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography (HD-OCT). METHODS: In this observational case-control study, 104 eyes (53 FECD eyes and 51 healthy controls) received HD-OCT imaging (Envisu R2210, Bioptigen, Buffalo Grove, IL, USA) using a 6 mm radial scan pattern centered on the corneal vertex. FECD was clinically categorized into early (without corneal edema) and late-stage (with corneal edema). A total of 18,720 anterior segment optical coherence tomography (AS-OCT) images (9180 healthy; 5400 early-stage FECD; 4140 late-stage FECD) of 104 eyes (81 patients) were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema. Using 5-fold cross-validation on the dataset containing 11,340 OCT images (63 eyes), the network was trained with 80% of these images (3420 healthy; 3060 early-stage FECD; 2700 late-stage FECD), then tested with 20% (720 healthy; 720 early-stage FECD; 720 late-stage FECD). Thereafter, a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes (5040 healthy; 1620 early-stage FECD 720 late-stage FECD). Visualization of learned features was done, and area under curve (AUC), specificity, and sensitivity of the prediction outputs for healthy, early and late-stage FECD were computed. RESULTS: The final model achieved an AUC of 0.997 ± 0.005 with 91% sensitivity and 97% specificity in detecting early-FECD; an AUC of 0.974 ± 0.005 with a specificity of 92% and a sensitivity up to 100% in detecting late-stage FECD; and an AUC of 0.998 ± 0.001 with a specificity 98% and a sensitivity of 99% in discriminating healthy corneas from all FECD. CONCLUSION: Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.

Surgical outcomes of isolated lens coloboma with or without cataract among young adults.

OBJECTIVE: To evaluate the visual and refractive outcomes after phacoemulsification surgery in eyes with isolated lens coloboma. DESIGN: Prospective, consecutive case series. PARTICIPANTS: Eighteen eyes with isolated lens coloboma of 13 patients were included in the study. Mean patient age was 13.9 ± 6.5 years. METHODS: Patients underwent phacoemulsification surgery, with combined implantation of capsular tension ring (CTR) and intraocular lens. In colobomas of less than 120°, a CTR was used, whereas in colobomas of more than 120°, a Cionni-modified single eyelet CTR was used to achieve better capsular centration. The main outcome measures were uncorrected distance visual acuity, corrected distance visual acuity, refraction, and keratometry. RESULTS: Mean logMAR uncorrected distance visual acuity and corrected distance visual acuity improved significantly from 1.53 ± 0.35 and 1.02 ± 0.47 before surgery to 0.67 ± 0.51 and 0.52 ± 0.49 at the last visit of the follow-up (p < 0.001). Mean refractive cylinder and spherical equivalent decreased significantly from -6.73 ± 1.73 and -6.72 ± 4.07 D preoperatively to -1.40 ± 1.39 and -0.83 ± 1.31 D at the end of the follow-up (p = 0.001 and p = 0.01, respectively). Mean keratometric astigmatism at preoperative and postoperative visits were 1.58 ± 0.97 and 1.65 ± 0.94 D, respectively (p = 0.70). CONCLUSIONS: Phacoemulsification with CTR and intraocular lens implantation is an effective and safe option for providing a refractive correction and a significant visual improvement in eyes with isolated lens coloboma.

Enhanced depth imaging optical coherence tomography findings in Behçet disease.

PURPOSE: Investigation of subfoveal choroidal thickness in Behçet disease (BD) with enhanced depth imaging optical coherence tomography (EDI-OCT). METHODS: The study included 35 patients with posterior uveitis (PU) associated with BD, 35 patients with BD without ocular involvement, and 30 healthy controls. RESULTS: There were no significant differences between the groups in terms of age, gender, axial length, or spherical equivalent refractive error. The mean subfoveal choroidal thickness was thinner in eyes with PU than in eyes without PU and healthy controls (p = 0.026). CONCLUSION: EDI-OCT is a beneficial test for evaluating choroid morphology in BD. Thinning of the subfoveal choroidal tissue has been observed in patients with BD-associated PU.