Abstract
Background
Conventional cytogenetic analysis remains central to the diagnosis and risk stratification of hematological malignancies but is constrained by labor-intensive workflows, inter-observer variability, and sensitivity to image quality. Although artificial intelligence (AI) approaches have been proposed for individual analytical tasks, clinically integrated, end-to-end pipelines aligned with reporting standards remain limited.Methods
We developed and evaluated a clinically oriented, AI-assisted cytogenetic analysis pipeline integrating image enhancement, chromosome detection, numerical and targeted structural abnormality assessment, and standardized reporting. Image quality was enhanced using a Pix2Pix-based generative model, followed by chromosome localization with a YOLOv8 detector and structural classification using a Siamese ResNet-18 architecture. Outputs were translated into ISCN-formatted reports aligned with College of American Pathologists (CAP) requirements. The system was evaluated retrospectively on clinical bone marrow karyogram datasets, with performance assessed using image-level fidelity metrics, analytical performance, and system-level feasibility under expert oversight.Results
Image enhancement demonstrated high structural fidelity (mean SSIM >0.98). Within a gated, quality-controlled pipeline, chromosome detection achieved robust performance, and the structural classifier showed strong discriminative ability for the targeted abnormality t(9;22). At the case level, primary concordance with expert interpretation was 85.8%, increasing to 92.35% following adjudication of clinically acceptable reporting differences. Automated generation of CAP-aligned ISCN reports was achieved under predefined quality-control constraints with mandatory expert validation.Conclusions
This study presents a clinically grounded, proof-of-concept AI-assisted cytogenetic decision-support pipeline integrating enhancement, gated analysis, abnormality assessment, and structured reporting. While demonstrating encouraging system-level performance, the framework is intended as a decision-support tool and remains at the stage of controlled feasibility evaluation. Further prospective validation, expanded structural coverage, and deployment-level governance will be required before clinical implementation.