An approach to reduce Descemet's membrane scrolling: Relevance to Descemet's membrane endothelial keratoplasty (DMEK).

Purpose: We aimed to determine whether Descemet's membrane (DM) scrolling occurs primarily along the vertical or horizontal axis and establish whether oval trephination along the axis of least scrolling can reduce the grade of the scroll. Methods: The longest limbus-to-limbus axis on 28 sclerocorneal discs was taken as the horizontal axis. The horizontal (n = 7) or (right angles to it) vertical (n = 6) axis was marked on DM before peeling it off. The direction and grade of scrolling was observed. Narrow strips (3-4 mm wide) were then cut along the two axes (n = 4 each) and the scrolling pattern was observed. Ellipses (7 × 9 mm) of DM were punched along the two axes (n = 6 each) and the scrolls graded. Immunofluorescent staining for elastin on horizontal and vertical tissue sections from three DM samples was performed. The intensity and thickness of elastin staining were measured. Results: Twenty-four (85.72%) DM samples showed scrolling along the horizontal axis, none showed scrolling along the vertical axis, and four (14.28%) samples showed a spiral scroll, regardless of which axis was marked (grade 3.7 and 3.6). Vertically oval discs showed significantly reduced scrolling (grade 1.2) compared to horizontally oval discs (grade 3.5). Narrow strips of DM showed a similar scrolling pattern. Immunohistology showed no difference in any of the parameters examined along the two axes or from the center to the periphery. Conclusion: DM scrolls primarily along the horizontal axis. Vertically oval DM samples show minimal scrolling, which can be an advantage in DMEK. Differential scrolling is not determined by the distribution of elastin.

Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol.

INTRODUCTION: Infectious keratitis (IK) represents the fifth-leading cause of blindness worldwide. A delay in diagnosis is often a major factor in progression to irreversible visual impairment and/or blindness from IK. The diagnostic challenge is further compounded by low microbiological culture yield, long turnaround time, poorly differentiated clinical features and polymicrobial infections. In recent years, deep learning (DL), a subfield of artificial intelligence, has rapidly emerged as a promising tool in assisting automated medical diagnosis, clinical triage and decision-making, and improving workflow efficiency in healthcare services. Recent studies have demonstrated the potential of using DL in assisting the diagnosis of IK, though the accuracy remains to be elucidated. This systematic review and meta-analysis aims to critically examine and compare the performance of various DL models with clinical experts and/or microbiological results (the current 'gold standard') in diagnosing IK, with an aim to inform practice on the clinical applicability and deployment of DL-assisted diagnostic models. METHODS AND ANALYSIS: This review will consider studies that included application of any DL models to diagnose patients with suspected IK, encompassing bacterial, fungal, protozoal and/or viral origins. We will search various electronic databases, including EMBASE and MEDLINE, and trial registries. There will be no restriction to the language and publication date. Two independent reviewers will assess the titles, abstracts and full-text articles. Extracted data will include details of each primary studies, including title, year of publication, authors, types of DL models used, populations, sample size, decision threshold and diagnostic performance. We will perform meta-analyses for the included primary studies when there are sufficient similarities in outcome reporting. ETHICS AND DISSEMINATION: No ethical approval is required for this systematic review. We plan to disseminate our findings via presentation/publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022348596.