Practical Challenges for the Implementation of AI-Based Image Analysis in Ophthalmology Research: Insights and...

PURPOSE: To share first experience from the Swiss RetinAI Consortium in applying artificial intelligence (AI)-based optical coherence tomography (OCT) analysis across multiple centres, to highlight practical barriers encountered during implementation, and to outline practical recommendations for future collaborative multicentre AI-based OCT research.

METHODS: The Swiss RetinAI Consortium, consisting of six ophthalmology centres, implemented the FDA-cleared Discovery platform (RetinAI, Bern, Switzerland) for collaborative OCT analysis. Challenges encountered during protocol development for OCT export, anonymisation, data upload, AI-based analysis, and data sharing were systematically collected from all sites involved. These included regulatory, technical, and methodological aspects. The findings were discussed at expert meetings and consolidated into shared guidance for data acquisition and processing. The aim was to provide practical recommendations to support standardised workflows in future multicentre AI-driven research.

IDENTIFIED CHALLENGES: Key regulatory, technical, and methodological challenges were identified during multicentre implementation. These included the legal requirement for Swiss-based server hosting, incomplete anonymisation due to heterogeneous export protocols, and non-standardised OCT protocols treated as being equivalent, despite covering different retinal areas. Additional barriers were inter-device variability, ETDRS grid misalignment without the option for manual correction, major segmentation errors requiring extensive review, and unfiltered data exports that often exceeded one million entries, thereby increasing the risk of readout errors. Moreover, the absence of automated quality screening and the lack of cross-centre reproducibility data were identified as important methodological gaps. Together, these challenges shaped our practical recommendations for reliable multicentre AI-based OCT analysis.

PRACTICAL RECOMMENDATIONS: Based on the identified challenges, we derive practical recommendations focusing on standardised anonymised data export procedures, harmonised OCT acquisition protocols, mandatory quality control steps for grid placement and segmentation, and structured strategies for handling large-scale output data. Our recommendations offer a practical roadmap to support future collaborative multicentre AI-based OCT research.