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Abstract：
Artificial intelligence (AI)-based clinical decision support tools, being developed across multiple fields in medicine, need to be evaluated for their impact on the treatment and outcomes of patients as well as optimisation of the clinical workflow. The RAZORBILL study will investigate the impact of advanced AI segmentation algorithms on the disease activity assessment in patients with neovascular age-related macular degeneration (nAMD) by enriching three-dimensional (3D) retinal optical coherence tomography (OCT) scans with automated fluid and layer quantification measurements.
RAZORBILL is an observational, multicentre, multinational, open-label study, comprising two phases: (a) clinical data collection (phase I): an observational study design, which enforces neither strict visit schedule nor mandated treatment regimen was chosen as an appropriate design to collect data in a real-world clinical setting to enable evaluation in phase II and (b) OCT enrichment analysis (phase II): de-identified 3D OCT scans will be evaluated for disease activity. Within this evaluation, investigators will review the scans once enriched with segmentation results (i.e., highlighted and quantified pathological fluid volumes) and once in its original (i.e., non-enriched) state. This review will be performed using an integrated crossover design, where investigators are used as their own controls allowing the analysis to account for differences in expertise and individual disease activity definitions.
In order to apply novel AI tools to routine clinical care, their benefit as well as operational feasibility need to be carefully investigated. RAZORBILL will inform on the value of AI-based clinical decision support tools. It will clarify if these can be implemented in clinical treatment of patients with nAMD and whether it allows for optimisation of individualised treatment in routine clinical care.
RAZORBILL is an observational, multicentre, multinational, open-label study, comprising two phases: (a) clinical data collection (phase I): an observational study design, which enforces neither strict visit schedule nor mandated treatment regimen was chosen as an appropriate design to collect data in a real-world clinical setting to enable evaluation in phase II and (b) OCT enrichment analysis (phase II): de-identified 3D OCT scans will be evaluated for disease activity. Within this evaluation, investigators will review the scans once enriched with segmentation results (i.e., highlighted and quantified pathological fluid volumes) and once in its original (i.e., non-enriched) state. This review will be performed using an integrated crossover design, where investigators are used as their own controls allowing the analysis to account for differences in expertise and individual disease activity definitions.
In order to apply novel AI tools to routine clinical care, their benefit as well as operational feasibility need to be carefully investigated. RAZORBILL will inform on the value of AI-based clinical decision support tools. It will clarify if these can be implemented in clinical treatment of patients with nAMD and whether it allows for optimisation of individualised treatment in routine clinical care.
摘要：
背景：基于人工智能（AI）的临床决策支持工具，在医学的多个领域发展，需要评估它们对患者治疗和结果的影响以及临床工作流程的优化。RAZORBILL研究将通过丰富三维（3D）视网膜光学相干断层扫描（OCT）扫描与自动流体和层量化测量，研究先进的AI分割算法对新生血管性年龄相关性黄斑变性（nAMD）患者疾病活动评估的影响。
方法：RAZORBILL是一种观察，多中心,跨国公司,开放标签研究，包括两个阶段：（a）临床数据收集（第一阶段）：观察性研究设计，该方案既没有强制严格的就诊时间表,也没有强制的治疗方案被选择为在现实世界的临床环境中收集数据的合适设计,以便能够在II期进行评估和(b)OCT富集分析(II期):去识别的3DOCT扫描将评估疾病活动.在这次评估中,调查人员将审查扫描，一旦丰富了分割结果(即，突出显示和量化的病理液体积)，并且一次是原始的(即，非丰富)状态。本审查将使用集成的交叉设计，研究者被用作他们自己的对照，允许分析考虑专业知识和个体疾病活动定义的差异。
结论：为了将新型AI工具应用于常规临床护理，他们的利益以及运营可行性需要仔细调查。RAZORBILL将告知基于AI的临床决策支持工具的价值。它将阐明这些是否可以在nAMD患者的临床治疗中实施，以及是否可以在常规临床护理中优化个性化治疗。
方法：RAZORBILL是一种观察，多中心,跨国公司,开放标签研究，包括两个阶段：（a）临床数据收集（第一阶段）：观察性研究设计，该方案既没有强制严格的就诊时间表,也没有强制的治疗方案被选择为在现实世界的临床环境中收集数据的合适设计,以便能够在II期进行评估和(b)OCT富集分析(II期):去识别的3DOCT扫描将评估疾病活动.在这次评估中,调查人员将审查扫描，一旦丰富了分割结果(即，突出显示和量化的病理液体积)，并且一次是原始的(即，非丰富)状态。本审查将使用集成的交叉设计，研究者被用作他们自己的对照，允许分析考虑专业知识和个体疾病活动定义的差异。
结论：为了将新型AI工具应用于常规临床护理，他们的利益以及运营可行性需要仔细调查。RAZORBILL将告知基于AI的临床决策支持工具的价值。它将阐明这些是否可以在nAMD患者的临床治疗中实施，以及是否可以在常规临床护理中优化个性化治疗。
