Vienna Clinical Trial Centre (VTC), Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
Eye (Lond). 2023 May;37(7):1464-1469. doi: 10.1038/s41433-022-02154-8. Epub 2022 Jul 5.
In neovascular age-related macular degeneration (nAMD) the exact amount of fluid and its location on optical coherence tomography (OCT) have been defined as crucial biomarkers for disease activity and therapeutic decisions. Yet in the absence of quantitative evaluation tools, real-world care outcomes are disappointing. Artificial intelligence (AI) offers a practical option for clinicians to enhance point-of-care management by analysing OCT volumes in a short time. In this protocol we present the prospective implementation of an AI-algorithm providing automated real-time fluid quantifications in a clinical real-world setting.
This is a prospective, multicentre, randomized (1:1) and double masked phase III clinical trial. Two-hundred-ninety patients with active nAMD will be randomized between a study arm using AI-supported fluid quantifications and another arm using conventional qualitative assessments, i.e. state-of-the-art disease management. The primary outcome is defined as the mean number of injections over 1 year. Change in BCVA is defined as a secondary outcome.
Automated measurement of fluid volumes in all retinal compartments such as intraretinal fluid (IRF), and subretinal fluid (SRF) will serve as an objective tool for clinical investigators on which to base retreatment decisions. Compared to qualitative fluid assessment, retreatment decisions will be plausible and less prone to error or large variability. The underlying hypothesis is that fluid should be treated, while residual persistent or stable amounts of fluid may not benefit from further therapy. Reducing injection numbers without diminishing the visual benefit will increase overall patient safety and relieve the burden for healthcare providers.
TRIAL-REGISTRATION: EudraCT-Number: 2019-003133-42.
在新生血管性年龄相关性黄斑变性（nAMD）中，光学相干断层扫描（OCT）上的液体量及其位置已被确定为疾病活动和治疗决策的关键生物标志物。然而，由于缺乏定量评估工具，现实世界的护理结果令人失望。人工智能（AI）为临床医生提供了一种实用的选择，通过在短时间内分析 OCT 容积来增强即时护理管理。在本方案中，我们提出了在临床现实环境中前瞻性实施人工智能算法，提供自动实时液量定量的方案。
这是一项前瞻性、多中心、随机（1:1）、双盲 III 期临床试验。将 290 名活动性 nAMD 患者随机分为使用 AI 支持的液量定量的研究组和另一个使用传统定性评估的组，即最先进的疾病管理。主要结局定义为 1 年内的平均注射次数。BCVA 的变化被定义为次要结局。
自动测量所有视网膜腔室（如视网膜内液 [IRF] 和视网膜下液 [SRF]）中的液体量将作为临床研究者的客观工具，用于制定再治疗决策。与定性的液体评估相比，再治疗决策将更加合理，且不太容易出错或出现较大的变异性。其基本假设是，应该对液体进行治疗，而残留的持续或稳定的液体量可能不需要进一步治疗。减少注射次数而不降低视觉获益将提高整体患者安全性，并减轻医疗保健提供者的负担。
EudraCT 编号：2019-003133-42。
