人工智能促进老年性黄斑变性的临床试验招募工作

IF 3.2 Q1 OPHTHALMOLOGY Ophthalmology science Pub Date : 2024-06-19 DOI:10.1016/j.xops.2024.100566

Dominic J. Williamson MSc , Robbert R. Struyven MD , Fares Antaki MD , Mark A. Chia MD , Siegfried K. Wagner MD, PhD , Mahima Jhingan MBBS , Zhichao Wu PhD , Robyn Guymer MBBS, PhD , Simon S. Skene PhD , Naaman Tammuz PhD , Blaise Thomson PhD , Reena Chopra PhD , Pearse A. Keane MD

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引用次数: 0

摘要

目的人工智能（AI）的最新发展使其能够改变临床试验过程的多个阶段。在本研究中，我们探讨了人工智能在老年性黄斑变性晚期患者地理萎缩（GA）的临床试验招募中的作用。研究对象英国伦敦莫菲尔德眼科医院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OCT成像的306 651名疑似视网膜疾病患者（602 826只眼睛）。方法利用人工智能生成的视网膜组织分割，对OCT扫描进行深度学习模型训练，以识别可能符合GA试验条件的患者。该方法的功效与传统的基于关键字的电子健康记录（EHR）搜索进行了比较。主要结果测量主要结果包括人工智能在识别符合试验条件的患者方面的积极预测值，次要结果是专家在FAF上分割的GA区域与人工智能分割的OCT扫描之间的类内相关性。结果与电子病历搜索（693 例，阳性预测值：40%；95% 置信区间 [CI]：39%-42%）相比，人工智能系统以更高的精确度筛选出了更多符合条件的患者（1139 例，阳性预测值：63%；95% 置信区间 [CI]：54%-71%）。人工智能与电子病历相结合的方法确定了 604 名符合条件的患者，阳性预测值为 86%（95% CI：79%-92%）。在符合试验标准的病例中，FAF 上分割的 GA 面积与 OCT 上 AI 分割的面积的类内相关性为 0.77（95% CI：0.68-0.84）。该人工智能还能根据几项临床试验的不同成像标准进行调整，生成从 438 到 1817 例患者的定制短名单。一旦有了治疗方法，类似的人工智能系统也可用于识别可能从治疗中获益的个体。

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Artificial Intelligence to Facilitate Clinical Trial Recruitment in Age-Related Macular Degeneration

Objective

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.

Design

Cross-sectional study.

Subjects

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.

Methods

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.

Main Outcome Measures

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.

Results

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.

Conclusions

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.