Case Series: Unbiased Deep Sequencing Analysis of Acute Infectious Conjunctivitis in an Ambulatory Eye Center in Berkeley, California.

SIGNIFICANCE: Acute infectious conjunctivitis poses significant challenges to eye care providers. It can be highly transmissible, and because etiology is often presumed, correct treatment and management can be difficult. This study uses unbiased deep sequencing to identify causative pathogens of infectious conjunctivitis, potentially allowing for improved approaches to diagnosis and management. PURPOSES: This study aimed to identify associated pathogens of acute infectious conjunctivitis in a single ambulatory eye care center. CASE REPORTS: This study included patients who presented to the University of California Berkeley eye center with signs and symptoms suggestive of infectious conjunctivitis. From December 2021 to July 2021, samples were collected from seven subjects (ages ranging from 18 to 38). Deep sequencing identified associated pathogens in five of seven samples, including human adenovirus D, Haemophilus influenzae , Chlamydia trachomatis , and human coronavirus 229E. CONCLUSIONS: Unbiased deep sequencing identified some unexpected pathogens in subjects with acute infectious conjunctivitis. Human adenovirus D was recovered from only one patient in this series. Although all samples were obtained during the COVID-19 pandemic, only one case of human coronavirus 229E and no SARS-CoV-2 were identified.

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning.

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging.

A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema.

IMPORTANCE: Diabetic macular edema is one of the leading causes of vision loss among working-age adults in the United States. Telemedicine screening programs and epidemiological studies rely on monoscopic fundus photography for the detection of clinically significant macular edema (CSME). Improving the accuracy of detecting CSME from monoscopic images could be valuable while recognizing the limitations of such detection in an era of optical coherence tomography detection of diabetic macular edema. OBJECTIVE: To evaluate the screening test accuracy of radially arranged sectors affected by hard exudates in the detection of CSME. DESIGN, SETTING, AND PARTICIPANTS: This investigation was a cross-sectional study of CSME grading in monoscopic images using a sectors approach. The Early Treatment Diabetic Retinopathy Study criteria were used to confirm the presence of CSME by the following 2 methods: stereoscopic fundus photography (method 1) and dilated biomicroscopy in combination with optical coherence tomography (method 2). Participants were recruited at a university-based practice between June 14, 2014, and December 28, 2015. MAIN OUTCOMES AND MEASURES: Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: A total of 207 eyes from an ethnically/racially diverse group of 207 patients (mean [SD] age, 53.6 [10.8] years; 58.9% [122 of 207] female) were included in the analysis. Twelve eyes (5.8%) were diagnosed as having CSME based on method 1. The intermethod and intergrader agreement for CSME diagnosis and sector count was substantial (κ range, 0.66 [95% CI, 0.47-0.85] to 0.75 [95% CI, 0.53-0.97]; P < .001 for all). Area under the receiver operating characteristic curve was 93.2% (95% CI, 84.2%-100%) when evaluating a sectors approach against method 1 as a reference test and offered up to an 8.6% (95% CI, 3.0%-14.3%) increase in specificity compared with the existing methods of detection. The positive predictive value was 33.3% (95% CI, 25.6%-45.5%), and the negative predictive value was 98.1% (95% CI, 96.9%-100%). The results were similar when comparing a sectors approach with method 2 as a reference test. CONCLUSIONS AND RELEVANCE: A sectors approach shows good screening test characteristics for the detection of CSME. Its implementation in the existing telemedicine programs would require minimal resources. This approach will have the greatest effect in a setting where implementation of optical coherence tomography, a more objective and sensitive way to detect retinal thickening, is not feasible. The proposed method also may be easily incorporated in the automated diabetic retinopathy detection algorithms.