Williamson Dominic J, Struyven Robbert R, Antaki Fares, Chia Mark A, Wagner Siegfried K, Jhingan Mahima, Wu Zhichao, Guymer Robyn, Skene Simon S, Tammuz Naaman, Thomson Blaise, Chopra Reena, Keane Pearse A
NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
Centre for Medical Image Computing, University College London, London, UK.
Ophthalmol Sci. 2024 Jun 19;4(6):100566. doi: 10.1016/j.xops.2024.100566. eCollection 2024 Nov-Dec.
Recent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition.
Cross-sectional study.
Retrospective dataset from the INSIGHT Health Data Research Hub at Moorfields Eye Hospital in London, United Kingdom, including 306 651 patients (602 826 eyes) with suspected retinal disease who underwent OCT imaging between January 1, 2008 and April 10, 2023.
A deep learning model was trained on OCT scans to identify patients potentially eligible for GA trials, using AI-generated segmentations of retinal tissue. This method's efficacy was compared against a traditional keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value of this approach, by comparing AI predictions with expert assessments.
The primary outcomes included the positive predictive value of AI in identifying trial-eligible patients, and the secondary outcome was the intraclass correlation between GA areas segmented on FAF by experts and AI-segmented OCT scans.
The AI system shortlisted a larger number of eligible patients with greater precision (1139, positive predictive value: 63%; 95% confidence interval [CI]: 54%-71%) compared with the EHR search (693, positive predictive value: 40%; 95% CI: 39%-42%). A combined AI-EHR approach identified 604 eligible patients with a positive predictive value of 86% (95% CI: 79%-92%). Intraclass correlation of GA area segmented on FAF versus AI-segmented area on OCT was 0.77 (95% CI: 0.68-0.84) for cases meeting trial criteria. The AI also adjusts to the distinct imaging criteria from several clinical trials, generating tailored shortlists ranging from 438 to 1817 patients.
This study demonstrates the potential for AI in facilitating automated prescreening for clinical trials in GA, enabling site feasibility assessments, data-driven protocol design, and cost reduction. Once treatments are available, similar AI systems could also be used to identify individuals who may benefit from treatment.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
人工智能（AI）的最新发展使其有能力改变临床试验过程的多个阶段。在本研究中，我们探讨了在针对年龄相关性黄斑变性晚期——地理萎缩（GA）的众多正在进行的临床试验中，AI在GA患者临床试验招募中的作用。
横断面研究。
来自英国伦敦穆尔菲尔兹眼科医院INSIGHT健康数据研究中心的回顾性数据集，包括2008年1月1日至2023年4月10日期间接受光学相干断层扫描（OCT）成像的306651例疑似视网膜疾病患者（602826只眼）。
使用AI生成的视网膜组织分割图，在OCT扫描上训练深度学习模型，以识别可能符合GA试验条件的患者。将该方法的疗效与基于传统关键词的电子健康记录（EHR）搜索进行比较。通过将AI预测结果与专家评估结果进行比较，使用眼底自发荧光（FAF）图像进行临床验证，以计算该方法的阳性预测值。
主要观察指标包括AI识别符合试验条件患者的阳性预测值，次要观察指标是专家在FAF上分割的GA区域与AI分割的OCT扫描区域之间的组内相关性。
与EHR搜索（693例，阳性预测值：40%；95%置信区间[CI]：39%-42%）相比，AI系统筛选出了更多符合条件且精度更高的患者（1139例，阳性预测值：63%；95%CI：54%-71%）。AI-EHR联合方法识别出604例符合条件的患者，阳性预测值为86%（95%CI：79%-92%）。对于符合试验标准的病例，FAF上分割的GA区域与OCT上AI分割区域的组内相关性为0.77（95%CI：0.68-0.84）。AI还能根据多个临床试验的不同成像标准进行调整，生成438至1817例患者的定制候选名单。
本研究证明了AI在促进GA临床试验自动预筛选、实现研究点可行性评估、数据驱动的方案设计及降低成本方面的潜力。一旦有了治疗方法，类似的AI系统也可用于识别可能从治疗中受益的个体。
专有或商业披露信息可在本文末尾的脚注和披露部分中找到。
