Corneal Microaneurysm-A Vascular Feature of Conjunctival Squamous Intraepithelial Neoplasia.

PURPOSE: The diagnosis of conjunctival squamous intraepithelial neoplasia (CSIN) can be difficult because of the heterogeneous appearance. Despite established risk factors and diagnostic support by high-resolution optical coherence tomography (hrOCT) and indocyanine green angiography (ICGA), the only reliable diagnostic method is a histological work-up. This case report is the first to describe corneal microaneurysms in CSIN as a vascular feature for conjunctival tumor angiogenesis. METHODS: An 84-year-old male patient was referred with a suspected diagnosis of pterygium. Biomicroscopic examination revealed a whitish epithelial lesion of conjunctival origin with centripetal corneal growth and extension over 5 limbal hours. Intralesional vascularization showed highly altered morphology with aneurysmal changes. After imaging with hrOCT and ICGA, excision was performed in a "no-touch double-freeze and thaw" technique, followed by histological and immunohistochemical work-up. RESULTS: hrOCT showed an epithelial, hyperreflective lesion with a maximum thickness of 272 µm and sharp central border. ICGA confirmed active perfusion and partial thrombosis of the aneurysmal terminal vascular buds dilated to 405 µm with early dye leakage within the first minute. Histological examination confirmed the clinical diagnosis of CSIN with focal high-grade dysplasia. Postoperatively, there was no recurrence during the observation period of 5 months. CONCLUSIONS: Intralesional terminal microaneurysms are a feature of tumor angiogenesis in CSIN. The relevance and frequency of this potential new risk factor for malignancy should be investigated in further studies.

Transforming Tele-Ophthalmology: Utilizing Cloud Computing for Remote Eye Care.

BACKGROUND: Tele-ophthalmology is gaining recognition for its role in improving eye care accessibility via cloud-based solutions. The Google Cloud Platform (GCP) Healthcare API enables secure and efficient management of medical image data such as high-resolution ophthalmic images. OBJECTIVES: This study investigates cloud-based solutions' effectiveness in tele-ophthalmology, with a focus on GCP's role in data management, annotation, and integration for a novel imaging device. METHODS: Leveraging the Integrating the Healthcare Enterprise (IHE) Eye Care profile, the cloud platform was utilized as a PACS and integrated with the Open Health Imaging Foundation (OHIF) Viewer for image display and annotation capabilities for ophthalmic images. RESULTS: The setup of a GCP DICOM storage and the OHIF Viewer facilitated remote image data analytics. Prolonged loading times and relatively large individual image file sizes indicated system challenges. CONCLUSION: Cloud platforms have the potential to ease distributed data analytics, as needed for efficient tele-ophthalmology scenarios in research and clinical practice, by providing scalable and secure image management solutions.

Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment.

INTRODUCTION: The aim of this work is to identify patients at risk of limited access to healthcare through artificial intelligence using a name-ethnicity classifier (NEC) analyzing the clinical stage of cataract at diagnosis and preoperative visual acuity. METHODS: This retrospective, cross-sectional study includes patients seen in the cataract clinic of a tertiary care hospital between September 2017 and February 2020 with subsequent cataract surgery in at least one eye. We analyzed 4971 patients and 8542 eyes undergoing surgery. RESULTS: The NEC identified 360 patients with names classified as 'non-German' compared to 4611 classified as 'German'. Advanced cataract (7 vs. 5%; p = 0.025) was significantly associated with group 'non-German'. Mean best-corrected visual acuity in group 'non-German' was 0.464 ± 0.406 (LogMAR), and in group 'German' was 0.420 ± 0.334 (p = 0.009). This difference remained significant after exclusion of patients with non-lenticular ocular comorbidities. Surgical time and intraoperative complications did not differ between the groups. Retrobulbar or general anesthesia was chosen significantly more frequently over topical anesthesia in group 'non-German' compared to group 'German' (24 vs. 18% respectively; p < 0.001). CONCLUSIONS: This study shows that artificial intelligence is able to uncover health disparities between people with German compared to non-German names using NECs. Patients with non-German names, possibly facing various social barriers to healthcare access such as language barriers, have more advanced cataracts and worse visual acuity upon presentation. Artificial intelligence may prove useful for healthcare providers to discover and counteract such inequalities and establish tailored preventive measures to decrease morbidity in vulnerable population subgroups.