Liu Siyin, Kandakji Lynn, Stupnicki Aleksander, Sumodhee Dayyanah, Leucci Marcello T, Hau Scott, Balal Shafi, Okonkwo Arthur, Moghul Ismail, Kanda Sandor P, Allan Bruce D, Gore Dan M, Muthusamy Kirithika, Hardcastle Alison J, Davidson Alice E, Liskova Petra, Pontikos Nikolas
University College London Institute of Ophthalmology, London, UK.
Moorfields Eye Hospital NHS Foundation Trust, London, UK.
Transl Vis Sci Technol. 2025 Jun 2;14(6):12. doi: 10.1167/tvst.14.6.12.
Fuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on artificial intelligence (AI) models developed for the diagnosis and management of FECD.
We conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded the included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations.
Nineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only three studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies.
Despite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility.
This review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.
富克斯角膜内皮营养不良（FECD）是一种常见的、与年龄相关的视力损害原因。本系统评价综合了文献中有关为FECD的诊断和管理而开发的人工智能（AI）模型的证据。
我们于2000年1月1日至2024年6月31日在MEDLINE、PubMed、科学网和Scopus中进行了系统的文献检索。纳入了在FECD管理的各种临床背景下利用AI的全文研究。数据提取涵盖模型开发、预测结果、验证和模型性能指标。我们使用诊断准确性研究质量评估2工具对纳入的研究进行分级。本评价遵循系统评价和Meta分析的首选报告项目（PRISMA）建议。
分析了19项研究。在FECD诊断和管理中应用的主要AI算法包括专门用于计算机视觉的神经网络架构，用于共焦或镜面显微镜图像或眼前段光学相干断层扫描图像。AI被应用于多种临床背景，如评估角膜内皮和水肿以及预测角膜移植术后移植物脱离和存活情况。尽管许多研究报告了有前景的模型性能，但一个显著的局限性是只有三项研究进行了外部验证。纳入研究中患者选择过程和实验设计所引入的偏倚很明显。
尽管AI算法有增强FECD诊断和预后评估的潜力，但仍需要进一步开展工作来评估其在现实世界中的适用性和临床效用。
本评价为研究人员、临床医生和政策制定者提供了重要见解，有助于他们了解FECD管理中现有的AI研究，并指导未来的卫生服务策略。
