Coelomycetous fungi are among the emerging causes of infections and have been involved in many kinds of infections, including keratitis and endophtalmitis. Here, we present the first case of keratitis caused by Neocucurbitaria unguis-hominis, a coelomycetous fungus belonging to the family Cucurbitariaceae. In this case report, we describe the clinical presentation of a 56-year-old woman, a regular contact lens wearer, who was treated for pain in her right eye and fixed spot vision after an injury with plant debris. On examination, a corneal ulcer was observed, the foreign body was removed, and topical eye-drop therapy was started. After an initial improvement, the patient returned three weeks later due to a recurrence of discomfort in her right eye, observing the persistence of the corneal ulcer. Corneal scrapings were taken for culture, growing a filamentous fungus after seven days, which was identified by sequencing the fungal internal transcribed spacer region. It should be noted that microbiological identification of the coelomycetes in the clinical laboratory is not easy because of their difficulty in sporulating, making molecular techniques based on the amplification and sequencing of appropriate phylogenetic markers essential. Identification of these fungi is mandatory in order to optimise treatment due to the difficulty in eradicating them with antifungal treatment, requiring surgery in 50% of cases.

Fuchs' endothelial corneal dystrophy (FECD) increases the risk of corneal decompensation after cataract surgery, often leading to endothelial keratoplasty (EK). Reliable prediction of EK is essential for surgical planning, yet traditional corneal biomarkers have limited prognostic value. We present a novel deep learning framework based on Scheimpflug tomographic imaging to improve FECD prognosis. The approach integrates three components to address data limitations: (1) clinical domain knowledge, (2) ensemble learning, and (3) self-supervised learning (SSL). A hybrid convolutional neural network (CNN) is proposed, combining a RANSAC-based algorithm for estimating best-fit sphere (BFS) elevation maps with a dual-branch design that incorporates Polar Pooling to mimic clinical reasoning. Robustness is enhanced through bootstrap aggregation, where multiple models are trained on different data subsets and their predictions averaged. To further reduce reliance on annotated data, we introduce a self-supervised contrastive pretraining task. This task distinguishes images from the same eye across time, using a weighted contrastive loss that emphasizes corneal irregularities. On a multi-center dataset, the framework achieved an AUC of 0.94, outperforming biomarker-based models and demonstrating strong generalizability across hospitals. The proposed methodology provides a robust and interpretable decision-support tool for FECD management and cataract surgery planning.