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

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by Innovate UK grant number 1008100. Dr. Keane is supported by UK Research & Innovation Future Leaders Fellowship (MR/T019050/1). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the UK Health Research Authority (reference: 20/HRA/2158, approved 5 May 2020). Informed consent was waived, given that our study pertains to retrospective anonymized data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.