Topical tacrolimus for high-risk corneal transplantation: a randomized, clinical trial.

BACKGROUND: The prevalence of rejection is 10-30% in penetrating keratoplasty (PKP) case, and the rate is higher in cases of high-risk patients. Although using topical corticosteroids is a standard method for management the rejection of post-PKP patients, it may not be sufficiently potent in high-risk patients. Topical administration of tacrolimus (TAC) may be effective in suppression rejection after corneal transplantation. This study aimed to investigate the efficacy and safety of topical TAC in high-risk PKP patients in Japan. METHODS: This study was a single centre, single-blinded, randomized controlled trial. Patients with a history of PKP, graft rejection, atopic dermatitis, or deep corneal neovascularisation who underwent PKP were enrolled. They were randomly assigned to receive 0.1% TAC ophthalmic suspension or artificial tear (AT) up to week 52 after surgery. All participants received 0.1% betamethasone up to week 13 after surgery then they received 0.1% fluorometholone up to week 52. The incidence of immunological rejection during the observation period was the main outcome measure in this study. RESULTS: Thirty patients were enrolled in this study, and 12 eyes in the TAC group and 13 eyes in the AT group completed the study, respectively. Five out of 30 patients discontinued participation after providing informed consent. No serious adverse effects were developed in patients who received 0.1% TAC ophthalmic suspension. No rejection episodes occurred in the TAC group, while one eye in the AT group had rejection. Graft clarity, best spectacle-corrected visual acuity, intraocular pressure, and corneal endothelial cell density were not significantly different between the TAC and AT groups. CONCLUSION: Our results demonstrated that good tolerability of 0.1% TAC ophthalmic suspension. However, we failed to demonstrate its efficacy in preventing immunological rejection in high-risk patients undergoing PKP. TRIAL REGISTRATION: This study was first registered in the University Hospital Medical Information Network (UMIN000029669, Date of registration: November 1, 2017). With the enforcement of the Clinical Trial Act in Japan, the study re-registered in the Japan Registry of Clinical Trials (jRCTs031180342, Date of registration: March 18, 2019).

Analysis of the performance of the CorneAI for iOS in the classification of corneal diseases and cataracts based on journal photographs.

CorneAI for iOS is an artificial intelligence (AI) application to classify the condition of the cornea and cataract into nine categories: normal, infectious keratitis, non-infection keratitis, scar, tumor, deposit, acute primary angle closure, lens opacity, and bullous keratopathy. We evaluated its performance to classify multiple conditions of the cornea and cataract of various races in images published in the Cornea journal. The positive predictive value (PPV) of the top classification with the highest predictive score was 0.75, and the PPV for the top three classifications exceeded 0.80. For individual diseases, the highest PPVs were 0.91, 0.73, 0.42, 0.72, 0.77, and 0.55 for infectious keratitis, normal, non-infection keratitis, scar, tumor, and deposit, respectively. CorneAI for iOS achieved an area under the receiver operating characteristic curve of 0.78 (95% confidence interval [CI] 0.5-1.0) for normal, 0.76 (95% CI 0.67-0.85) for infectious keratitis, 0.81 (95% CI 0.64-0.97) for non-infection keratitis, 0.55 (95% CI 0.41-0.69) for scar, 0.62 (95% CI 0.27-0.97) for tumor, and 0.71 (95% CI 0.53-0.89) for deposit. CorneAI performed well in classifying various conditions of the cornea and cataract when used to diagnose journal images, including those with variable imaging conditions, ethnicities, and rare cases.

Does Corneal Topography Using 3-Dimensional Optical Coherence Tomography Suggest Different Subtypes of Terrien Marginal Degeneration?

PURPOSE: The aim of this study was to analyze corneal topography relative to astigmatism, higher order aberrations, and corneal curvatures in Terrien marginal degeneration using 3-dimensional anterior-segment optical coherence tomography. METHODS: Twenty-nine eyes of 15 Finnish patients from a tertiary referral center had topographic axial power maps classified into 4 patterns by visual grading: crab claw (CC), mixed (M), arcuate (A), and normal. Regular astigmatism, keratometry, higher order aberrations, maximal corneal thinning, apex thickness, and curvature changes relative to best fit sphere toward maximal peripheral thinning were compared. RESULTS: Four, 9, and 12 eyes were classified as CC, M, and A, respectively; 1 as normal with clinical disease; and 3 as normal with unilateral disease. Median follow-up was 2.3 (range, 0-7.2) years. Three eyes changed pattern. Patients with the CC pattern were the youngest when diagnosed, progressed more rapidly, exhibited cavities in superior quadrants with anterior bulging, and had greater higher order posterior aberrations. Patients with the M pattern were older, progressed slower, and showed superonasal asymmetric corneal steepening extending centrally, often with asymmetric bow tie. Patients with pattern A showed little progression and were the oldest when diagnosed, with maximal corneal thinning equally in all quadrants. According to the Wang classification, the median stage was 4, 2, and 2 in CC, M, and A patterns, respectively, whereas it was always 2 by the Süveges classification. CONCLUSIONS: Terrien marginal degeneration is characterized by distinct corneal topographic patterns that differ in tomographic features, suggesting existence of subtypes in addition to different stages of disease. Patients representing CC and M patterns might benefit from more frequent monitoring.

Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases.

AIM: To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. METHODS: This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases. RESULTS: The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for 'urgent', 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for 'semi-urgent', 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for 'routine' and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for 'observation', respectively. CONCLUSIONS: The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.