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一种用于治疗新生血管性年龄相关性黄斑变性的人工智能决策工具的安全性和有效性,以及对临床路径整合和实施的探索:一项多方法验证研究的方案。

Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study.

机构信息

Population Health Sciences Institute, University of Newcastle upon Tyne, Newcastle upon Tyne, UK

Newcastle Eye Centre, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.

出版信息

BMJ Open. 2023 Feb 1;13(2):e069443. doi: 10.1136/bmjopen-2022-069443.

Abstract

INTRODUCTION

Neovascular age-related macular degeneration (nAMD) management is one of the largest single-disease contributors to hospital outpatient appointments. Partial automation of nAMD treatment decisions could reduce demands on clinician time. Established artificial intelligence (AI)-enabled retinal imaging analysis tools, could be applied to this use-case, but are not yet validated for it. A primary qualitative investigation of stakeholder perceptions of such an AI-enabled decision tool is also absent. This multi-methods study aims to establish the safety and efficacy of an AI-enabled decision tool for nAMD treatment decisions and understand where on the clinical pathway it could sit and what factors are likely to influence its implementation.

METHODS AND ANALYSIS

Single-centre retrospective imaging and clinical data will be collected from nAMD clinic visits at a National Health Service (NHS) teaching hospital ophthalmology service, including judgements of nAMD disease stability or activity made in real-world consultant-led-care. Dataset size will be set by a power calculation using the first 127 randomly sampled eligible clinic visits. An AI-enabled retinal segmentation tool and a rule-based decision tree will independently analyse imaging data to report nAMD stability or activity for each of these clinic visits. Independently, an external reading centre will receive both clinical and imaging data to generate an enhanced reference standard for each clinic visit. The non-inferiority of the relative negative predictive value of AI-enabled reports on disease activity relative to consultant-led-care judgements will then be tested. In parallel, approximately 40 semi-structured interviews will be conducted with key nAMD service stakeholders, including patients. Transcripts will be coded using a theoretical framework and thematic analysis will follow.

ETHICS AND DISSEMINATION

NHS Research Ethics Committee and UK Health Research Authority approvals are in place (21/NW/0138). Informed consent is planned for interview participants only. Written and oral dissemination is planned to public, clinical, academic and commercial stakeholders.

摘要

简介

新生血管性年龄相关性黄斑变性(nAMD)的治疗管理是导致医院门诊预约量最大的单一疾病之一。nAMD 治疗决策的部分自动化可以减少对临床医生时间的需求。已建立的人工智能(AI)驱动的视网膜成像分析工具可应用于这种情况,但尚未对此进行验证。目前还缺乏对利益相关者对这种 AI 决策工具的看法的初步定性调查。本多方法研究旨在确定用于 nAMD 治疗决策的 AI 决策工具的安全性和有效性,并了解它在临床路径中的位置以及可能影响其实施的因素。

方法和分析

将从 NHS 教学医院眼科服务的 nAMD 诊所就诊中收集单中心回顾性成像和临床数据,包括在真实世界的顾问主导的护理中对 nAMD 疾病稳定性或活动性的判断。数据集大小将通过使用前 127 个随机抽样的合格诊所就诊进行计算。一个 AI 驱动的视网膜分割工具和一个基于规则的决策树将分别分析成像数据,以报告这些诊所就诊中的每个 nAMD 稳定性或活动性。独立地,外部阅读中心将接收临床和成像数据,为每个诊所就诊生成增强的参考标准。然后,将测试 AI 报告的疾病活动性的相对负预测值相对于顾问主导护理判断的非劣效性。同时,将对大约 40 名关键 nAMD 服务利益相关者(包括患者)进行半结构化访谈。将使用理论框架对转录本进行编码,并进行主题分析。

伦理和传播

已获得 NHS 研究伦理委员会和英国健康研究管理局的批准(21/NW/0138)。仅计划对访谈参与者进行知情同意。计划向公众、临床、学术和商业利益相关者进行书面和口头传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460b/9896175/58cf0c14596a/bmjopen-2022-069443f01.jpg

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