PURPOSE: To evaluate the association of scleral contact lens (SCL) use on the risk for keratoplasty for people with keratoconus. METHODS: The electronic health records of patients receiving eye care at the University of Michigan Kellogg Eye Center between August 1, 2012, and December 31, 2018, were reviewed. Patients with a diagnostic code of keratoconus or corneal ectasia, no previous history of keratoplasty, and for whom data were available for both eyes were included. Using a multivariable Cox regression model, associations between SCL use and keratoplasty were tested and adjusted for sociodemographic factors, maximum keratometry, and current contact lens (CL) use. RESULTS: Two thousand eight hundred six eyes met the inclusion criteria. CL use in each eye was 36.2% with no CL, 7.2% soft, 33.9% rigid gas permeable (RGP), and 22.7% scleral. A total of 3.2% of eyes underwent keratoplasty. In the adjusted model, SCL or RGP CL use significantly lowered the hazard of undergoing keratoplasty (HR = 0.19, 95% confidence interval [CI] 0.09-0.39, P < 0.0001 and HR = 0.30, 95% CI 0.17-0.52, P < 0.0001, respectively) when compared with no CL use. Factors associated with increased risk of keratoplasty were black race as compared to white (HR = 1.87, 95% CI 1.10-3.16, P = 0.02), younger age (HR = 0.92 per 5-year increment, 95% CI 0.86-0.99, P = 0.032), and lower socioeconomic status (HR = 1.08 per 5-point increase in the Area Deprivation Index, 95% CI 1.03-1.13, P = 0.0008). Keratoplasty was not associated with sex, insurance, or maximum keratometry. CONCLUSIONS: Physicians should maximize the use of scleral or RGP CL because patients who successfully use CL have almost one-fifth the risk of undergoing keratoplasty.

PURPOSE: There is a need to understand physicians diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling. METHODS: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm 2 central, <2 mm 2 peripheral, and >8 mm 2 central). RESULTS: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would never and always treat a <2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections ( P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States ( P < 0.001) but not international clinicians. CONCLUSIONS: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians treatment decisions.

PURPOSE: This study sought to identify the sources of differential performance and misclassification error among local (Indian) and external (non-Indian) corneal specialists in identifying bacterial and fungal keratitis based on corneal photography. METHODS: This study is a secondary analysis of survey data assessing the ability of corneal specialists to identify acute bacterial versus fungal keratitis by using corneal photography. One-hundred images of 100 eyes from 100 patients with acute bacterial or fungal keratitis in South India were previously presented to an international cohort of cornea specialists for interpretation over the span of April to July 2021. Each expert provided a predicted probability that the ulcer was either bacterial or fungal. Using these data, we performed multivariable linear regression to identify factors predictive of expert performance, accounting for primary practice location and surrogate measures to infer local fungal ulcer prevalence, including locality, latitude, and dew point. In addition, Brier score decomposition was used to determine experts reliability (calibration) and resolution (boldness) and were compared between local (Indian) and external (non-Indian) experts. RESULTS: Sixty-six experts from 16 countries participated. Indian practice location was the only independently significant predictor of performance in multivariable linear regression. Resolution among Indian experts was significantly better (0.08) than among non-Indian experts (0.01; P < 0.001), indicating greater confidence in their predictions. There was no significant difference in reliability between the two groups ( P = 0.40). CONCLUSION: Local cornea experts outperformed their international counterparts independent of regional variability in tropical risk factors for fungal keratitis. This may be explained by regional characteristics of infectious ulcers with which local corneal specialists are familiar.

Purpose

High-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging.

Methods

The system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity.

Results

The system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%.

Conclusions

Automation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening.

Translational relevance

Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening.

This study quantifies the performance of an international cohort of cornea specialists in image-based differentiation of bacterial and fungal keratitis, identifying significant regional variation and establishing a reference standard for comparison against machine learning models.