Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY

Translational Vision Science & Technology Pub Date : 2025-06-02 DOI:10.1167/tvst.14.6.12

Siyin Liu, Lynn Kandakji, Aleksander Stupnicki, Dayyanah Sumodhee, Marcello T Leucci, Scott Hau, Shafi Balal, Arthur Okonkwo, Ismail Moghul, Sandor P Kanda, Bruce D Allan, Dan M Gore, Kirithika Muthusamy, Alison J Hardcastle, Alice E Davidson, Petra Liskova, Nikolas Pontikos

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引用次数: 0

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Translational relevance: 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.

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人工智能在富氏角膜内皮营养不良中的应用综述

目的：富克斯内皮性角膜营养不良（FECD）是一种常见的、与年龄相关的视力损害原因。本系统综述综合了用于诊断和管理FECD的人工智能（AI）模型的文献证据。方法：系统检索2000年1月1日至2024年6月31日的MEDLINE、PubMed、Web of Science、Scopus等文献。本文收录了利用人工智能进行FECD管理的各种临床研究的全文。数据提取包括模型开发、预测结果、验证和模型性能度量。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。本综述遵循系统评价和荟萃分析（PRISMA）推荐的首选报告项目。结果：共分析了19项研究。应用于FECD诊断和管理的主要人工智能算法包括专门用于计算机视觉的神经网络架构，用于共聚焦或镜面显微镜图像，或前段光学相干断层扫描图像。人工智能应用于多种临床环境，如评估角膜内皮和水肿，预测角膜移植后移植物脱离和存活。尽管许多研究报告了有希望的模型性能，但一个显著的限制是只有三个研究进行了外部验证。在纳入的研究中，由患者选择过程和实验设计引入的偏倚是明显的。结论：尽管人工智能算法具有增强FECD诊断和预后的潜力，但需要进一步的工作来评估其现实世界的适用性和临床实用性。转化相关性：本综述为研究人员、临床医生和政策制定者提供了重要见解，帮助他们理解FECD管理中现有的人工智能研究，并指导未来的卫生服务战略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.