Complications of Adenoviral Keratoconjunctivitis in Ophthalmologists and Orthoptists: Epidemiology and Risk Factor, A Retrospective Questionnaire Analysis.

PURPOSE: The purpose of this study was to assess the medical history of adenoviral keratoconjunctivitis (AK) and subepithelial infiltrates (SEIs) among French ophthalmologists and orthoptists and the frequency of unreported occupational diseases. We also described short-term and long-term consequences of AK and evaluated associated factors. METHODS: The REDCap questionnaire was diffused online several times over 7 consecutive months, from October 2019 to May 2020, through mailing lists (French Society of Ophthalmology, residents, and hospital departments), social networks, and by word of mouth. RESULTS: Seven hundred ten participants were included with a response rate of 6.2% for ophthalmologists, 3.8% for orthoptists, and 28.3% for ophthalmology residents. The medical history of AK was found in 24.1% (95% confidence interval 21%-27.2%) of respondents and SEI in 43.9% (36.5%-51.3%) of the AK population. In total, 87.1% (82.1%-92.1%) of AK occupational diseases were not declared. In total, 57.7% of respondents took 9.4 ± 6.2 days of sick leave, mostly unofficial, and 95.7% stopped surgeries for 13.0 ± 6.6 days. Among the AK population, 39.8% had current sequelae, with 17.5% having persistent SEIs, 19.9% using current therapy, and 16.4% experiencing continuing discomfort. SEIs were associated with wearing contact lenses (odds ratio 3.31, 95% confidence interval 1.19-9.21) and smoking (4.07, 1.30-12.8). Corticosteroid therapy was associated with a greater number of sequelae (3.84, 1.51-9.75). CONCLUSIONS: AK and SEI affect a large proportion of ophthalmologists and orthoptists, possibly for years, with high morbidity leading to occupational discomfort. Few practitioners asked for either to be recognized as an occupational disease. Associated factors would require a dedicated study.

Conjunctival limbal autografting (CLAU) combined with customised simple limbal epithelial transplantation (SLET) in a severe corneal chemical burn: Case report.

PURPOSE: The most common cause of severe limbal stem cell deficiency (LSCD) is chemical injury. We report a case of severe unilateral alkali injury complicated by total LSCD, treated with customized Simple limbal epithelial transplantation (SLET) combined with conjunctival-limbal autografting (CLAU). OBSERVATION: A 23-year-old female sustained a severe unilateral alkali chemical injury, resulting in total LSCD, treated with customized SLET combined with CLAU. We used two autologous limbal biopsies harvested from the fellow eye. One was used for CLAU and the second was split into multiple pieces, and was glued to bare stroma following resection of the corneal pannus. A stable ocular surface was achieved, and the donor eye remained healthy. Visual acuity improved from hand motion initially, to 20/20 over one year post-operatively. CONCLUSION AND IMPORTANCE: This case report demonstrates the efficacy and safety of customized SLET with supplemental CLAU to treat total LSCD in severe ocular burns.

Corneal Topography Raw Data Classification Using a Convolutional Neural Network.

PURPOSE: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS). DESIGN: Retrospective machine-learning experimental study. METHODS: A total of 3,000 Orbscan examinations (1,000 of each class) of different patients of our institution were selected for model training and validation. One hundred examinations of each class were randomly assigned to the test set. For each examination, the raw numerical data from "elevation against the anterior best fit sphere (BFS)," "elevation against the posterior BFS" "axial anterior curvature," and "pachymetry" maps were used. Each map was a square matrix of 2,500 values. The 4 maps were stacked and used as if they were 4 channels of a single image.A convolutional neural network was built and trained on the training set. Classification accuracy and class wise sensitivity and specificity were calculated for the validation set. RESULTS: Overall classification accuracy of the validation set (n = 300) was 99.3% (98.3%-100%). Sensitivity and specificity were, respectively, 100% and 100% for KC, 100% and 99% (94.9%-100%) for normal examinations, and 98% (97.4%-100%) and 100% for RS examinations. CONCLUSION: Using combined corneal topography raw data with a convolutional neural network is an effective way to classify examinations and probably the most thorough way to automatically analyze corneal topography. It should be considered for other routine tasks performed on corneal topography, such as refractive surgery screening.