Ocular surface squamous neoplasia (OSSN) has different treatment modalities. Although surgical excision has been the gold standard therapeutic option, topical pharmacotherapy agents such as 5-fluorouracil (5-FU), interferon alfa-2b (IFN) and mitomycin-C (MMC) are also commonly used. The protocol was registered (CRD42021224961). Comprehensive literature research was carried out to compare topical pharmacotherapy (5-FU or IFN or MMC) to surgical excision regarding clinical success (tumor resolution), recurrence and complications in patients undergoing treatment for OSSN. From 7859 records, 7 articles were included in the qualitative and 4 in the quantitative synthesis. The outcomes of surgical excision and topical pharmacotherapy were comparable in the included articles. There were no significant differences between surgical excision and topical pharmacotherapy regarding the clinical success [odds ratio (OR): 0.785; confidence interval (CI): 0.130–4.736, P = 0.792)] and tumor recurrence (OR: 0.746; CI: 0.213–2.609; P = 0.646). The most common side effect of the different therapeutic options was dry eye. The highest rate of dry eye symptoms was reported after surgical excision (in 59%). Topical pharmacotherapy with all the 3 agents is as effective and well-tolerable as surgical excision in terms of tumor resolution, recurrence rate and side effects in all OSSN patients suggesting similar long-term clinical benefits.

To provide an artificial intelligence (AI) method using in vivo confocal microscopy (IVCM) to differentiate ocular surface squamous neoplasia (OSSN) from other lesions and compare the performance of well-known AI-related solutions. A dataset of 2,774 IVCM images, comprising OSSN and other ocular surface diseases was used to train three deep learning models: ResNet50V2, Yolov8x, and VGG19. These models were trained to identify OSSN-related lesions by recognizing specific visual features, including the “starry-sky” pattern, hyperkeratosis, mitotic figures and irregularly shaped epithelial cells. To mitigate class imbalance, a novel square-based data augmentation strategy was employed. Additionally, we implemented a few-shot learning model to enhance the precision of rare symptoms, such as mitosis. To enhance model interpretation, Shapley values and Uniform Manifold Approximation and Projection (UMAP) analysis were employed to explain decision-making processes. The AI models demonstrated high accuracy in distinguishing healthy tissues from pathological ones, achieving over 90% accuracy across all models. In our binary classification task, all AI models had accuracy above 97% (precision ≥ 98%, recall ≥ 85%, F1 score ≥ 92%). The model achieved lower accuracy in 4 class labeled classification. Aggregation of cell-level results provided the best performance with an F1 score of 100%. The models successfully identified patient-specific features in IVCM images, suggesting that these images can act as “fingerprints”. Our AI model utilizing IVCM was able to classify OSSN with high accuracy. Moreover, cell-level classification results could be backpropagated to image-level and patient-level. The patient-specific information within IVCM images offers promise for personalized diagnostics and treatment monitoring in ocular oncology.

Purpose: To compare vergence, artificial intelligence, and combined intraocular lens (IOL) calculation formulas using a new swept-source optical coherence tomographer (SS-OCT) and to analyze their performance based on manifest and estimated refractive outcomes of cataract surgery. Setting: Department of Ophthalmology, University of Pécs Medical School, Pécs, Hungary. Design: Retrospective data analysis. Methods: Optical biometry readings of patients who underwent uneventful cataract removal and implantation of a monofocal acrylic IOL were used to predict IOL power with Barrett Universal II (BUII), Haigis, Hoffer Q, Holladay 1, Radial Basis Function (RBF) 2.0, Kane, Ladas Super Formula, and SRK/T. All the implanted IOLs were calculated by using the Haigis formula. The arithmetic prediction error and median and mean absolute refractive errors for all formulas were computed. The percentage of eyes within ±0.25 diopters (D), ±0.50 D, and ±1.0 D of prediction error was calculated. Results: A total of 95 eyes of 95 patients with a mean age of 68.80 ± 7.57 years were included. There was a statistically significant difference in absolute prediction error across the 8 IOL calculation formulas (P < .0001). Haigis showed the lowest mean absolute error, and it differed significantly from the BUII, Hoffer Q, Holladay 1, Ladas, RBF 2.0, and SRK/T formulas (P < .05). In terms of eyes within ±0.25 D, ±0.50 D, and ±1.0 D of prediction error, the Haigis formula showed the overall best performance. Conclusions: The results indicated that a recently developed SS-OCT provided accurate ocular biometry measurements before cataract surgery, and the Haigis formula incorporated in its software enabled precise calculation of IOL refractive power.

Purpose: The study was conducted to assess the central corneal thickness (CCT) of the healthy cornea with a recently developed noncontact specular microscope (EM-3000; Tomey) and compare the results with those measured with a contact specular microscope and an ultrasound pachymeter. Agreement between measurements taken by 2 investigators was also studied. Methods: The right eyes of 41 healthy individuals who had negative history of contact lens wear, ophthalmic disease, or ocular surgery were examined. The CCT was determined sequentially with a non-contact specular microscope, a contact specular microscope (EM-1000; Tomey), and an ultrasound pachymeter (AL-2000; Tomey). Each evaluation with the specular microscopes was performed by 2 independent operators. Results: A significant difference was detected in pachymetry measurements among the 3 instruments (p=0.01; analysis of variance). The mean CCT values were lower measured with the ultrasound pachymeter(537±30 μm) than the contact endothelial microscope (543±37 μm, p=0.17, Student t-test) and the noncontact microscope (549±33 μm, p<0.0001) (operator 1). There was no statistically significant difference in CCT measurements between the 2 endothelial microscopes (p=0.19). We found significant correlations (p<0.0001) in thickness measurements between each pair of instruments (r=0.91, noncontact microscopy and ultrasound pachymetry; r=0.74, noncontact and contact microscopy; r=0.72, contact microscopy and ultrasound pachymetry; Spearman rank correlation). Conclusions: The strong correlations among the 3 pachymetry devices suggest that the tested instruments provide reliable measurements; however, they cannot be used interchangeably.

Purpose: The purpose of this study was to evaluate corneal cellular and ultrastructural changes and to quantify the neuroinflammatory process in patients after mild severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: Thirty patients after SARS-CoV-2 infection and 41 age-matched controls were examined. All subjects underwent in vivo confocal microscopy of the corneal cell layers and subbasal nerve fibers with the Heidelberg Retina Tomograph II. Semiautomated analysis of basal epithelial, anterior and posterior stromal keratocyte, and endothelial cell density was performed. Dendritic cell (DC) density and area were also calculated, and subbasal nerve plexus morphology was analyzed. Results: The posterior stromal keratocyte density was significantly lower in patients after SARS-CoV-2 infection (P = 0.0006). DC density in the central cornea was significantly higher in patients after SARS-CoV-2 infection (P = 0.0004). There was a significant difference in the DC area between the 2 groups (P < 0.0001). Significantly altered subbasal nerve fiber morphology was detected in patients after SARS-CoV-2 infection compared with healthy volunteers (P < 0.05). Conclusions: Corneal cellular and ultrastructural changes demonstrated in this study suggest neuroinflammatory consequences of COVID-19 in the cornea in the absence of ophthalmoscopic alterations.

Purpose: The purpose of this study was to assess whether retinal microvascular or corneal nerve abnormalities occur earlier in diabetes mellitus (DM) and to identify imaging biomarkers in order to help prevent the subsequent irreversible retinal and corneal complications. Methods: The study comprised 35 eyes of 35 healthy volunteers and 52 eyes of 52 patients with type 1 and type 2 DM. Swept-source optical coherence tomography (OCT), OCT angiography, and in vivo corneal confocal microscopy were performed in both groups. Corneal sub-basal nerve plexus and vessel density (VD) of superficial capillary plexus (SCP) and deep capillary plexus (DCP) were evaluated. Results: All corneal sub-basal nerve fiber parameters were decreased in patients with DM compared with healthy subjects and the difference was significant for each result except for nerve fiber width (P = 0.586). No significant correlation was obtained between any nerve fiber morphology parameters and disease duration or HbA1C. VD in SCP was significantly decreased in the superior (P < 0.0001), temporal (P = 0.001), and nasal quadrant (P = 0.003) in the diabetes group. In DCP, only superior VD (P = 0.036), decreased significantly in the diabetes group. Ganglion cell layer thickness in the inner ring showed a significantly lower value in patients with DM (P < 0.0001). Conclusions: Our results implicate a more pronounced and earlier damage to the corneal nerve fibers compared to the retinal microvasculature in patients with DM. Translational Relevance: In DM, an earlier and more pronounced damage to the corneal nerve fibers was observed compared to the retinal microvasculature.