Artificial intelligence to facilitate clinical trial recruitment in age-related macular degeneration

Abstract

Background: 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. Methods: Using a diverse retrospective dataset from Moorfields Eye Hospital (London, United Kingdom) between 2008 and 2023 (602,826 eyes from 306,651 patients), we deployed a deep learning system trained on optical coherence tomography (OCT) scans to generate segmentations of the retinal tissue. AI outputs were used to identify a shortlist of patients with the highest likelihood of being eligible for GA clinical trials, and were compared to patients identified using a keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value (PPV) of this approach, by comparing AI predictions to expert assessments. Results: The AI system shortlisted a larger number of eligible patients with greater precision (1,139, PPV: 63%; 95% CI: 54-71%) compared to the EHR search (693, PPV: 40%; 95% CI: 39-42%). A combined AI-EHR approach identified 604 eligible patients with a PPV 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 1,817 patients. Conclusions: We demonstrate the potential for AI in facilitating automated pre-screening 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.