Does real-time artificial intelligence-based visual pathology enhancement of three-dimensional optical coherence tomography scans optimise treatment decision in patients with nAMD? Rationale and design of the RAZORBILL study

• Published in: British Journal of Ophthalmology
• Main Authors: Holz, Frank G, Abreu-Gonzalez, Rodrigo, Bandello, Francesco, Duval, Renaud, O'Toole, Louise, Pauleikhoff, Daniel, Staurenghi, Giovanni, Wolf, Armin, Lorand, Daniel, Clemens, Andreas, Gmeiner, Benjamin
• Format: Text
• Language: English
• Published: BMJ Publishing Group 1480
• Subjects: Clinical Science; Razorbill
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763175/; http://www.ncbi.nlm.nih.gov/pubmed/34362776; https://doi.org/10.1136/bjophthalmol-2021-319211

BACKGROUND/RATIONALE: 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. METHODS: 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. CONCLUSIONS: 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.