Clinical evaluation of AI-assisted quantitative marrow fibrosis assessment using Continuous Indexing of Fibrosis (CIF)

Assessment of fibrosis is central to the evaluation of diagnostic bone marrow trephine (BMT) biopsies. However, manual fibrosis grading is subjective and only semi-quantitative. We evaluated the clinical utility of a previously developed AI-based quantitative fibrosis assessment tool, Continuous Indexing of Fibrosis (CIF), using ~1000 consecutive BMT biopsies without pre-selection. An international panel of 14 haematopathologists performed manual reads using whole-slide images (WSI) of reticulin-stained slides and two types of CIF-assisted reads ( Sequential-assisted and Concurrent-assisted ) across three study rounds. The AI-derived CIF scores correlated strongly with the manual consensus MF grade (Spearman ρ = 0.770) and demonstrated good discriminative performance across adjacent MF grade boundaries. The CIF-assisted protocols significantly improved intra-observer and inter-observer agreement compared to manual assessment, without increasing read times. Sequential-assisted reads yielded the largest gain in inter-observer agreement (12.6 percentage points; 95% CI 10.5–14.8) and improved agreement with the consensus reference (3.0 percentage points; 95% CI 0.4–5.7). Both assisted protocols reduced discordance across the clinically significant MF-1 / MF-2 boundary. These findings demonstrate that AI-assisted quantitative bone marrow fibrosis assessment using CIF is reproducible, efficient and well suited for future clinical deployment testing. This supports a future role in standardising routine diagnostic haematopathology and enhancing the sensitivity of fibrosis-based clinical trial endpoints.